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menu-item-453"><a href="https://kdd2024.kdd.org/sponsors-and-partners/">Sponsors and Partners</a></li> </ul> </li> <li id="menu-item-123" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-123"><a href="https://kdd2024.kdd.org/organizing-committee/">Organizers</a></li> </ul></div> </nav> </div> </header> <main id="content" class="site-main post-958 page type-page status-publish hentry"> <div class="page-content"> <h2 class="wp-block-heading has-text-align-center">Research Track Papers Schedule</h2> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 11:00-13:00, Room 116<br>Theme: LLMs<br>Session Chair: Carl Yang (Emory University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>On Early Detection of Hallucinations in Factual Question Answering</strong></td></tr><tr><td>Ben Snyder (Amazon Web Services); Marius Moisescu (Amazon Web Services); Muhammad Bilal Zafar (Ruhr-Universit盲t Bochum, Research Center for Trustworthy Data Science and Security, University Alliance Ruhr)</td></tr><tr><td><strong>A Hierarchical Context Augmentation Method to Improve Retrieval-Augmented LLMs on Scientific Papers</strong></td></tr><tr><td>Tian-Yi Che (Beijing Institute of Technology); Xian-Ling Mao (Beijing Institute of Technology); Tian Lan (Beijing Institute of Technology); Heyan Huang (Beijing Institute of Technology)</td></tr><tr><td><strong>LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models</strong></td></tr><tr><td>Aoxiao Zhong (Harvard University, Alibaba Group); Dengyao Mo (Alibaba Group); Guiyang Liu (Alibaba Group); Jinbu Liu (Alibaba Group); Qingda Lu (Alibaba Group); Qi Zhou (Alibaba Group); Jiesheng Wu (Alibaba Group); Quanzheng Li (Harvard Medical School, Massachusetts General Hospital); Qingsong Wen (Alibaba Group)</td></tr><tr><td><strong>Efficient Mixture of Experts based on Large Language Models for Low-Resource Data Preprocessing</strong></td></tr><tr><td>Mengyi Yan (Beihang University); Yaoshu Wang (Shenzhen Institute of Computing Sciences); Kehan Pang (Beihang University); Xie Min (Shenzhen Institute of Computing Sciences); Jianxin Li (Beihang University)</td></tr><tr><td><strong>Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models</strong></td></tr><tr><td>Zhibo Hu (University of New South Wales, CSIRO Data61); Chen Wang (CSIRO Data61, University of New South Wales); Yanfeng Shu (CSIRO Data61); Hye-Young Paik (University of New South Wales); Liming Zhu (CSIRO Data61, University of New South Wales)</td></tr><tr><td><strong>Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network</strong></td></tr><tr><td>Lin Chen (Hong Kong University of Science and Technology); Fengli Xu (BNRist, Department of Electronic Engineering, Tsinghua University); Nian Li (Shenzhen International Graduate School, Tsinghua University); Zhenyu Han (BNRist, Department of Electronic Engineering, Tsinghua University); Meng Wang (Hefei University of Technology); Yong Li (BNRist, Department of Electronic Engineering, Tsinghua University); Pan Hui (Hong Kong University of Science and Technology (Guangzhou), Hong Kong University of Science and Technology)</td></tr><tr><td><strong>From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models</strong></td></tr><tr><td>Xumeng Wen (Microsoft Research Asia); Han Zhang (Tsinghua University); Shun Zheng (Microsoft Research Asia); Wei Xu (Tsinghua University); Jiang Bian (Microsoft Research Asia)</td></tr><tr><td><strong>Enhancing On-Device LLM Inference with Historical Cloud-Based LLM Interactions</strong></td></tr><tr><td>Yucheng Ding (Shanghai Jiao Tong University); Chaoyue Niu(Shanghai Jiao Tong University); Fan Wu(Shanghai Jiao Tong University); Shaojie Tang(University of Texas at Dallas); Chengfei Lyu(Alibaba Group); Guihai Chen(Shanghai Jiao Tong University)</td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 11:00-13:00, Room 120<br>Theme: Clustering & Community Detection<br>Session Chair: Yiu-ming Cheung (Hong Kong Baptist University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning</strong></td></tr><tr><td>Jintian Ji (Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education, School of Computer Science and Technology, Beijing Jiaotong University); Songhe Feng (Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education, School of Computer Science and Technology, Beijing Jiaotong University); Yidong Li (Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University), Ministry of Education, School of Computer Science and Technology, Beijing Jiaotong University)</td></tr><tr><td><strong>Topology-Driven Multi-View Clustering via Tensorial Refined Sigmoid Rank Minimization</strong></td></tr><tr><td>Zhibin Gu (Key Laboratory of Big Data & Artificial Intelligence in Transportation, (Beijing Jiaotong University), Ministry of Education, Beijing Jiaotong University, School of Computer Science and Technology); Zhendong Li (Key Laboratory of Big Data & Artificial Intelligence in Transportation, (Beijing Jiaotong University), Ministry of Education, Beijing Jiaotong University, School of Computer Science and Technology); Songhe Feng (Key Laboratory of Big Data & Artificial Intelligence in Transportation, (Beijing Jiaotong University), Ministry of Education, Beijing Jiaotong University, School of Computer Science and Technology)</td></tr><tr><td><strong>Resilient k-Clustering</strong></td></tr><tr><td>Sara Ahmadian (Google); MohammadHossein Bateni (Google); Hossein Esfandiari (Google); Silvio Lattanzi (Google); Morteza Monemizadeh (Department of Mathematics and Computer Science, TU Eindhoven); Ashkan Norouzi-Fard (Google)</td></tr><tr><td><strong>QGRL: Quaternion Graph Representation Learning for Heterogeneous Feature Data Clustering</strong></td></tr><tr><td>Junyang Chen (School of Computer Science and Technology, Guangdong University of Technology); Yuzhu Ji (School of Computer Science and Technology, Guangdong University of Technology); Rong Zou (Department of Computer Science, Hong Kong Baptist University); Yiqun Zhang (School of Computer Science and Technology, Guangdong University of Technology); Yiu-ming Cheung (Department of Computer Science, Hong Kong Baptist University)</td></tr><tr><td><strong>Effective Clustering on Large Attributed Bipartite Graphs</strong></td></tr><tr><td>Renchi Yang (Hong Kong Baptist University); Yidu Wu (Chinese University of Hong Kong); Xiaoyang Lin (Hong Kong Baptist University); Qichen Wang (Hong Kong Baptist University); Tsz Nam Chan (Shenzhen University); Jieming Shi (The Hong Kong Polytechnic University)</td></tr><tr><td><strong>ProCom: A Few-shot Targeted Community Detection Algorithm</strong></td></tr><tr><td>Xixi Wu (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Kaiyu Xiong (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Yun Xiong (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Xiaoxin He (National University of Singapore); Yao Zhang (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Yizhu Jiao (University of Illinois at Urbana-Champaign); Jiawei Zhang (IFM Lab, Department of Computer Science, University of California, Davis)</td></tr><tr><td><strong>Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation</strong></td></tr><tr><td>Meng Qin (Department of CSE, HKUST); Chaorui Zhang (Theory Lab, Huawei); Yu Gao (Theory Lab, Huawei); Weixi Zhang (Theory Lab, Huawei); Dit-Yan Yeung (Department of CSE, HKUST)</td></tr><tr><td><strong>Evading Community Detection via Counterfactual Neighborhood Search</strong></td></tr><tr><td>Andrea Bernini (Sapienza University of Rome); Fabrizio Silvestri (Sapienza University of Rome); Gabriele Tolomei (Sapienza University of Rome)</td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 11:00-13:00, Room 121<br>Theme: Time Series I (forecasting)<br>Session Chair: Sai Ravela (MIT)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>FRNet: Frequency-based Rotation Network for Long-term Time Series Forecasting<strong></strong></strong></td></tr><tr><td>Zhang Xinyu (Harbin Institute of Technology); Shanshan Feng (Centre for Frontier AI Research, A*STAR, Institute of High Performance Computing, A*STAR); Jianghong Ma (Harbin Institute of Technology); Huiwei Lin (Harbin Institute of Technology); Xutao Li (Harbin Institute of Technology); Yunming Ye (Harbin Institute of Technology); Li Fan (Hong Kong Polytechnic University); Yew Soon Ong (Centre for Frontier AI Research, A*STAR, Nanyang Technological University) </td></tr><tr><td><strong>Generative Pretrained Hierarchical Transformer for Time Series Forecasting<strong></strong></strong></td></tr><tr><td>Zhiding Liu (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Jiqian Yang (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Mingyue Cheng (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Yucong Luo (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Zhi Li (Shenzhen International Graduate School, Tsinghua University) </td></tr><tr><td><strong>Fredformer: Frequency Debiased Transformer for Time Series Forecasting<strong></strong></strong></td></tr><tr><td>Xihao Piao (SANKEN, Osaka University); Zheng Chen (SANKEN, Osaka University); Taichi Murayama (SANKEN, Osaka University); Yasuko Matsubara (SANKEN, Osaka University); Yasushi Sakurai (SANKEN, Osaka University) </td></tr><tr><td><strong>Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization<strong></strong></strong></td></tr><tr><td>Sheo Yon Jhin (Yonsei University); Seojin Kim (Yonsei University); Noseong Park (KAIST) </td></tr><tr><td><strong>GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing<strong></strong></strong></td></tr><tr><td>Chengqing Yu (Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences); Fei Wang (Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences); Zezhi Shao (Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences); Tangwen Qian (Institute of Computing Technology, Chinese Academy of Sciences); Zhao Zhang (Institute of Computing Technology, Chinese Academy of Sciences); Wei Wei (School of Computer Science and Technology, Huazhong University of Science and Technology); Yongjun Xu (Institute of Computing Technology, Chinese Academy of Sciences) </td></tr><tr><td><strong>AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting<strong></strong></strong></td></tr><tr><td>Raphael Fischer (Lamarr Institute for Machine Learning and Artificial Intelligence, TU Dortmund University); Amal Saadallah (Lamarr Institute for Machine Learning and Artificial Intelligence, TU Dortmund University) </td></tr><tr><td><strong>Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift<strong></strong></strong></td></tr><tr><td>Mouxiang Chen (Zhejiang University); Lefei Shen (Zhejiang University); Han Fu (Zhejiang University); Zhuo Li (State Street Technology (Zhejiang) Ltd.); Jianling Sun (Zhejiang University); Chenghao Liu (Salesforce Research Asia) </td></tr><tr><td><strong>RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms<strong></strong></strong></td></tr><tr><td>Luis Roque (LIACC/Faculty of Engineering, University of Porto); Carlos Soares (LIACC/Faculty of Engineering, University of Porto, Fraunhofer AICOS Portugal); Lu铆s Torgo (Dalhousie University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 11:00-13:00, Room 122<br>Theme: Probabilistic & Statistical methods<br>Session Chair: Matteo Riondato (Amherst College)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Statistical Models of Top-k Partial Orders with Variable k<strong></strong></strong></td></tr><tr><td>Amel Awadelkarim (Stanford University); Johan Ugander (Stanford University) </td></tr><tr><td><strong>Learning the Covariance of Treatment Effects Across Many Weak Experiments<strong></strong></strong></td></tr><tr><td>Aurelien Bibaut (Netflix); Winston Chou (Netflix); Simon Ejdemyr (Netflix); Nathan Kallus (Cornell University) </td></tr><tr><td><strong>Provable Adaptivity of Adam under Non-uniform Smoothness<strong></strong></strong></td></tr><tr><td>Bohan Wang (University of Science and Technology of China & Microsoft Research Asia); Yushun Zhang (The Chinese University of Hong Kong, Shenzhen); Huishuai Zhang (Peking University); Qi Meng (Chinese Academy of Mathematics and Systems Science); Ruoyu Sun (The Chinese University of Hong Kong, Shenzhen); Zhi-Ming Ma (Chinese Academy of Mathematics and Systems Science); Tie-Yan Liu (Microsoft); Zhi-Quan Luo (The Chinese University of Hong Kong, Shenzhen); Wei Chen (Institute of Computing Technology, Chinese Academy of Sciences) </td></tr><tr><td><strong>Label Shift Correction via Bidirectional Marginal Distribution Matching<strong></strong></strong></td></tr><tr><td>Ruidong Fan (National University of Defense Technology); Xiao Ouyang (National University of Defense Technology); Hong Tao (National University of Defense Technology); Chenping Hou (National University of Defense Technology) </td></tr><tr><td><strong>Top-Down Bayesian Posterior Sampling for Sum-Product Networks<strong></strong></strong></td></tr><tr><td>Soma Yokoi (The University of Tokyo); Issei Sato (The University of Tokyo) </td></tr><tr><td><strong>Estimated Judge Reliabilities for Weighted Bradley-Terry-Luce Are Not Reliable<strong></strong></strong></td></tr><tr><td>Andrew F. Dreher (The University of Texas at Austin); Etienne Vouga (The University of Texas at Austin); Donald S. Fussell (The University of Texas at Austin) </td></tr><tr><td><strong>Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals<strong></strong></strong></td></tr><tr><td>Marco Heyden (Karlsruhe Institute of Technology); Vadim Arzamasov (Karlsruhe Institute of Technology); Edouard Fouch茅 (Karlsruhe Institute of Technology); Klemens B枚hm (Karlsruhe Institute of Technology) </td></tr><tr><td><strong>A Uniformly Bounded Correlation Function for Spatial Point Patterns<strong></strong></strong></td></tr><tr><td>Evgenia Martynova (Radboud University Medical Center); Johannes Textor (Radboud University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 11:00-13:00, Room 129-130<br>Theme: Graph Neural Networks I<br>Session Chair: Chuan Shi (Beijing University of Posts and Telecomunications)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs<strong></strong></strong></td></tr><tr><td>Huanjing Zhao (Tsinghua University); Beining Yang (University of Edinburgh); Yukuo Cen (Zhipu AI); Junyu Ren (Software Engineering, Tsinghua University); Chenhui Zhang (Zhipu AI); Yuxiao Dong (Tsinghua University); Evgeny Kharlamov (Bosch Center for Artifcial Intelligence); Shu Zhao (Anhui University); Jie Tang (Tsinghua University) </td></tr><tr><td><strong>All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining<strong></strong></strong></td></tr><tr><td>Haihong Zhao (Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)); Aochuan Chen (Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)); Xiangguo Sun (Department of Systems Engineering and Engineering Management, and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong); Hong Cheng (Department of Systems Engineering and Engineering Management, and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong); Jia Li (Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)) </td></tr><tr><td><strong>Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks<strong></strong></strong></td></tr><tr><td>Yurui Lai (Hong Kong Baptist University); Xiaoyang Lin (Hong Kong Baptist University); Renchi Yang (Hong Kong Baptist University); Hongtao Wang (Hong Kong Baptist University) </td></tr><tr><td><strong>The Heterophily Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs<strong></strong></strong></td></tr><tr><td>Kun Wang (University of Science and Technology of China); Guibin Zhang (Tongji University); Xinnan Zhang (University of Minnesota); Junfeng Fang (University of Science and Technology of China); Xun Wu (Tsinghua University); Guohao Li (University of Oxford); Shirui Pan (Griffith University); Wei Huang (RIKEN AIP); Yuxuan Liang (The Hong Kong University of Science and Technology (Guangzhou)) </td></tr><tr><td><strong>Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space<strong></strong></strong></td></tr><tr><td>Ruikun Li (Shenzhen International Graduate School, Tsinghua University); Huandong Wang (Department of Electronic Engineering BNRist, Tsinghua University); Jinghua Piao (Department of Electronic Engineering BNRist, Tsinghua University); Qingmin Liao (Shenzhen International Graduate School, Tsinghua University); Yong Li (Department of Electronic Engineering BNRist, Tsinghua University) </td></tr><tr><td><strong>RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network<strong></strong></strong></td></tr><tr><td>Yunbo Hou (School of Software and Microelectronics, Peking University); Haoran Ye (National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University); Yingxue Zhang (Huawei Noah’s Ark Lab); Siyuan Xu (Huawei Noah’s Ark Lab); Guojie Song (National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University) </td></tr><tr><td><strong>AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation<strong></strong></strong></td></tr><tr><td>Weigang Lu (Xidian University); Ziyu Guan (Xidian University); Wei Zhao (Xidian University); Yaming Yang (Xidian University) </td></tr><tr><td><strong>GeoMix: Towards Geometry-Aware Data Augmentation<strong></strong></strong></td></tr><tr><td>Wentao Zhao (Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, Shanghai Jiaotong University); Qitian Wu (Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, Shanghai Jiaotong University); Chenxiao Yang (Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, Shanghai Jiaotong University); Junchi Yan (Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 11:00-13:00, Room 131-132<br>Theme: Rec Sys I<br>Session Chair: James Caverlee (Texas A&M University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-$n$ Recommendation<strong></strong></strong></td></tr><tr><td>Olivier Jeunen (ShareChat); Ivan Potapov (ShareChat); Aleksei Ustimenko (ShareChat) </td></tr><tr><td><strong>Double Correction Framework for Denoising Recommendation<strong></strong></strong></td></tr><tr><td>Zhuangzhuang He (Hefei University of Technology); Yifan Wang (Tsinghua University); Yonghui Yang (Hefei University of Technology); Peijie Sun (Tsinghua University); Le Wu (Hefei University of Technology, Institute of Dataspace, Hefei Comprehensive National Science Center); Bai Haoyue (Hefei University of Technology); Jinqi Gong (University of Macau); Richang Hong (Hefei University of Technology, Institute of Dataspace, Hefei Comprehensive National Science Center); Min Zhang (Tsinghua University) </td></tr><tr><td><strong>Mitigating Negative Transfer in Cross-Domain Recommendation via Knowledge Transferability Enhancement<strong></strong></strong></td></tr><tr><td>Zijian Song (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University); WenHan Zhang (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University); Lifang Deng (Lazada Group); Jiandong Zhang (Lazada Group); Wu Zhihua (Lazada Group); Kaigui Bian (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University); Bin Cui (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University) </td></tr><tr><td><strong>Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method<strong></strong></strong></td></tr><tr><td>Chen Yang (Nanbeige Lab, BOSS Zhipin, Gaoling School of Artificial Intelligence, Renmin University of China); Sunhao Dai (Gaoling School of Artificial Intelligence, Renmin University of China); Yupeng Hou (University of California, San Diego); Wayne Xin Zhao (Gaoling School of Artificial Intelligence, Renmin University of China); Jun Xu (Gaoling School of Artificial Intelligence, Renmin University of China); Yang Song (Nanbeige Lab, BOSS Zhipin); Hengshu Zhu (Career Science Lab, BOSS Zhipin) </td></tr><tr><td><strong>Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation<strong></strong></strong></td></tr><tr><td>Xiaocong Chen (Data 61, CSIRO); Siyu Wang (The University of New South Wales); Lina Yao (Data 61, CSIRO, The University of New South Wales) </td></tr><tr><td><strong>Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions<strong></strong></strong></td></tr><tr><td>Yaqing Wang (Baidu Research, Baidu Inc.); Hongming Piao (Department of Computer Science, City University of Hong Kong); Daxiang Dong (Baidu AI Cloud, Baidu Inc.); Quanming Yao (Department of Electronic Engineering, Tsinghua University); Jingbo Zhou (Baidu Research, Baidu Inc.) </td></tr><tr><td><strong>Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback<strong></strong></strong></td></tr><tr><td>Guipeng Xv (School of Informatics, Xiamen University); Xinyu Li (School of Informatics, Xiamen University); Ruobing Xie (Tencent); Chen Lin (School of Informatics, Xiamen University); Chong Liu (Tencent); Feng Xia (Tencent); Zhanhui Kang (Tencent); Leyu Lin (Tencent) </td></tr><tr><td><strong>Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias<strong></strong></strong></td></tr><tr><td>Miaomiao Cai (Hefei University of Technology); Lei Chen (Tsinghua University); Yifan Wang (DCST, Tsinghua University); Haoyue Bai (Hefei University of Technology); Peijie Sun (DCST, Tsinghua University); Le Wu (Hefei University of Technology); Min Zhang (DCST, Tsinghua University, Quan Cheng Laboratory); Meng Wang (Hefei University of Technology) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 11:00-13:00, Room 133<br>Theme: Scalable ML I </strong><br><strong>Session Chair: Geoff Webb (Monash University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Low Rank Multi-Dictionary Selection at Scale<strong></strong></strong></td></tr><tr><td>Boya Ma (Department of Computer Science, University at Albany, State University of New York); Maxwell McNeil (Department of Computer Science, University at Albany, State University of New York); Abram Magner (Department of Computer Science, University at Albany, State University of New York); Petko Bogdanov (Department of Computer Science, University at Albany, State University of New York) </td></tr><tr><td><strong>Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs<strong></strong></strong></td></tr><tr><td>Jiahuan Yan (Zhejiang University); Jintai Chen (University of Illinois at Urbana-Champaign); Qianxing Wang (Zhejiang University); Danny Z. Chen (University of Notre Dame); Jian Wu (Zhejiang University) </td></tr><tr><td><strong>Approximate Matrix Multiplication over Sliding Windows<strong></strong></strong></td></tr><tr><td>Ziqi Yao (East China Normal University); Lianzhi Li (East China Normal University); Mingsong Chen (East China Normal University); Xian Wei (East China Normal University); Cheng Chen (East China Normal University) </td></tr><tr><td><strong>Scalable Rule Lists Learning with Sampling<strong></strong></strong></td></tr><tr><td>Leonardo Pellegrina (Department of Information Engineering, University of Padova); Fabio Vandin (Department of Information Engineering, University of Padova) </td></tr><tr><td><strong>Efficient Exploration of the Rashomon Set of Rule Set Models<strong></strong></strong></td></tr><tr><td>Martino Ciaperoni (Aalto University); Han Xiao (The Upright Project); Aristides Gionis (KTH Royal Institute of Technology) </td></tr><tr><td><strong>Fast Multidimensional Partial Fourier Transform with Automatic Hyperparameter Selection<strong></strong></strong></td></tr><tr><td>Yong-chan Park (Seoul National University); Jongjin Kim (Seoul National University); U Kang (Seoul National University) </td></tr><tr><td><strong>Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization<strong></strong></strong></td></tr><tr><td>Andrea Agiollo (Department of Computer Science and Engineering, University of Bologna); Young In Kim (Department of Computer Science, Purdue University); Rajiv Khanna (Department of Computer Science, Purdue University) </td></tr><tr><td><strong>EcoVal: An Efficient Data Valuation Framework for Machine Learning<strong></strong></strong></td></tr><tr><td>Ayush Tarun (Ola Krutrim); Vikram Chundawat (RespAI Lab, India); Murari Mandal (RespAI Lab, India, Kalinga Institute of Industrial Technology (KIIT)); Hong Ming Tan (NUS Business School, National University of Singapore); Bowei Chen (Adam Smith Business School, University of Glasgow); Mohan Kankanhalli (National University of Singapore) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 11:00-13:00, Room 134<br>Theme: Computational Advertising </strong><br><strong>Session Chair: Goce Trajcevski (Iowa State University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Robust Auto-Bidding Strategies for Online Advertising<strong></strong></strong></td></tr><tr><td>Qilong Lin (Shanghai Jiao Tong University); Zhenzhe Zheng (Shanghai Jiao Tong University); Fan Wu (Shanghai Jiao Tong University) </td></tr><tr><td><strong>InLN: Knowledge-aware Incremental Leveling Network for Dynamic Advertising<strong></strong></strong></td></tr><tr><td>Xujia Li (Hong Kong University of Science and Technology); Jingshu Peng (Hong Kong University of Science and Technology); Lei Chen (Hong Kong University of Science and Technology (Guangzhou), Hong Kong University of Science and Technology) </td></tr><tr><td><strong>Joint Auction in the Online Advertising Market<strong></strong></strong></td></tr><tr><td>Zhen Zhang (Gaoling School of Artificial Intelligence, Renmin University of China); Weian Li (School of Software, Shandong University); Yahui Lei (Meituan Inc.); Bingzhe Wang (Gaoling School of Artificial Intelligence, Renmin University of China); Zhicheng Zhang (Gaoling School of Artificial Intelligence, Renmin University of China); Qi Qi (Gaoling School of Artificial Intelligence, Renmin University of China); Qiang Liu (Meituan Inc.); Xingxing Wang (Meituan Inc.) </td></tr><tr><td><strong>Auctions with LLM Summaries<strong></strong></strong></td></tr><tr><td>Avinava Dubey (Google Research); Zhe Feng (Google Research); Rahul Kidambi (Google Research); Aranyak Mehta (Google Research); Di Wang (Google Research) </td></tr><tr><td><strong>Bi-Objective Contract Allocation for Guaranteed Delivery Advertising<strong></strong></strong></td></tr><tr><td>Yan Li (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences); Yundu Huang (Alibaba Group); Wuyang Mao (Alibaba Group); Furong Ye (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences); Xiang He (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences); Zhonglin Zu (Alibaba Group); Shaowei Cai (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences) </td></tr><tr><td><strong>Optimized Cost Per Click in Online Advertising: A Theoretical Analysis<strong></strong></strong></td></tr><tr><td>Kaichen Zhang (Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)); Zixuan Yuan (Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)); Hui Xiong (Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Department of Computer Science and Engineering, Hong Kong University of Science and Technology) </td></tr><tr><td><strong>Truthful Bandit Mechanisms for Repeated Two-stage Ad Auctions<strong></strong></strong></td></tr><tr><td>Haoming Li (Shanghai Jiaotong University); Yumou Liu (The Chinese University of Hong Kong, Shenzhen); Zhenzhe Zheng (Shanghai Jiao Tong University); Zhilin Zhang (Alibaba Group); Jian Xu (Alibaba Group); Fan Wu (Shanghai Jiao Tong University) </td></tr><tr><td><strong>An Efficient Local Search Algorithm for Large GD Advertising Inventory Allocation with Multilinear Constraints<strong></strong></strong></td></tr><tr><td>Xiang He (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences); Wuyang Mao (Alibaba Group); Zhenghang Xu (Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education, Jilin University); Yuanzhe Gu (Alibaba Group); Yundu Huang (Alibaba Group); Zhonglin Zu (Alibaba Group); Liang Wang (Alibaba Group); Mengyu Zhao (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences); Mengchuan Zou (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 14:00-16:00, Room 116<br>Theme: Rec Sys II<br>Session Chair: Chen Ma (City University of Hong Kong)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Automatic Multi-Task Learning Framework with Neural Architecture Search in Recommendations</strong></td></tr><tr><td>Shen Jiang (State Key Laboratory for Novel Software Technology, Nanjing University); Guanghui Zhu (State Key Laboratory for Novel Software Technology, Nanjing University); Yue Wang (State Key Laboratory for Novel Software Technology, Nanjing University); Chunfeng Yuan (State Key Laboratory for Novel Software Technology, Nanjing University); Yihua Huang (State Key Laboratory for Novel Software Technology, Nanjing University)</td></tr><tr><td><strong>Continual Collaborative Distillation for Recommender System</strong></td></tr><tr><td>Gyuseok Lee (Pohang University of Science and Technology); SeongKu Kang (University of Illinois Urbana-Champaign); Wonbin Kweon (Pohang University of Science and Technology); Hwanjo Yu (Pohang University of Science and Technology)</td></tr><tr><td><strong>Relevance Meets Diversity: A User-Centric Framework for Knowledge Exploration Through Recommendations</strong></td></tr><tr><td>Erica Coppolillo (Department of Computer Science, University of Calabria, ICAR-CNR); Giuseppe Manco (ICAR-CNR); Aristides Gionis (KTH Royal Institute of Technology)</td></tr><tr><td><strong>Shopping Trajectory Representation Learning with Pre-training for E-commerce Customer Understanding and Recommendation</strong></td></tr><tr><td>Yankai Chen (Department of Computer Science and Engineering, The Chinese University of Hong Kong); Quoc-Tuan Truong (Amazon); Xin Shen (Amazon); Jin Li (Amazon); Irwin King (Department of Computer Science and Engineering, The Chinese University of Hong Kong)</td></tr><tr><td><strong>Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective</strong></td></tr><tr><td>Haotian Zhang (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Shuanghong Shen (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center); Bihan Xu (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Zhenya Huang (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center); Jinze Wu (iFLYTEK AI Research); Jing Sha (iFLYTEK AI Research); Shijin Wang (State Key Laboratory of Cognitive Intelligence, iFLYTEK AI Research)</td></tr><tr><td><strong>User Welfare Optimization in Recommender Systems with Competing Content Creators</strong></td></tr><tr><td>Fan Yao (University of Virginia); Yiming Liao (Meta Platforms, Inc.); Mingzhe Wu (University of Southern California); Chuanhao Li (Yale University); Yan Zhu (Google); James Yang (Meta Platforms, Inc.); Jingzhou Liu (Meta Platforms, Inc.); Qifan Wang (Meta Platforms, Inc.); Haifeng Xu (University of Chicago); Hongning Wang (University of Virginia)</td></tr><tr><td><strong>Conversational Dueling Bandits in Generalized Linear Models</strong></td></tr><tr><td>Shuhua Yang (University of Science and Technology of China); Hui Yuan (Princeton University); Xiaoying Zhang (ByteDance); Mengdi Wang (Princeton University); Hong Zhang (University of Science and Technology of China); Huazheng Wang (Oregon State University)</td></tr><tr><td><strong>DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation</strong></td></tr><tr><td>Kairui Fu (Zhejiang University); Shengyu Zhang (Zhejiang University, Shanghai Institute for Advanced Study of Zhejiang University); Zheqi Lv (Zhejiang University); Jingyuan Chen (Zhejiang University); Jiwei Li (Zhejiang University)</td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 14:00-16:00, Room 120<br>Theme: Graph clustering & Dense Subgraphs<br>Session Chair: Tim Weninger (University of Notre Dame)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>NeuroCut: A Neural Approach for Robust Graph Partitioning<strong></strong></strong></td></tr><tr><td>Rishi Shah (Department of Computer Science and Engineering, IIT Delhi); Krishnanshu Jain (Department of Computer Science and Engineering, IIT Delhi); Sahil Manchanda (Department of Computer Science and Engineering, IIT Delhi); Sourav Medya (University of Illinois at Chicago); Sayan Ranu (Department of Computer Science and Engineering, IIT Delhi) </td></tr><tr><td><strong>PSMC: Provable and Scalable Algorithms for Motif Conductance Based Graph Clustering<strong></strong></strong></td></tr><tr><td>Longlong Lin (College of Computer and Information Science, Southwest University); Tao Jia (College of Computer and Information Science, Southwest University); Zeli Wang (Chongqing University of Post and Telecommunications); Jin Zhao (School of Computer Science and Technology, Huazhong University of Science and Technology); Rong-Hua Li (Shenzhen Institute of Technology, Beijing Institute of Technology) </td></tr><tr><td><strong>Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective<strong></strong></strong></td></tr><tr><td>Yunfei Liu (Ant Group); Jintang Li (Ant Group); Chen Yuehe (Ant Group); Ruofan Wu (Ant Group); Ericbk Wang (Ant Group); Jing Zhou (Ant Group); Sheng Tian (Ant Group); Shuheng Shen (Ant Group); Xing Fu (Ant Group); Changhua Meng (Ant Group); Weiqiang Wang (Ant Group); Liang Chen (Unaffiliated) </td></tr><tr><td><strong>Expander Hierarchies for Normalized Cuts on Graphs<strong></strong></strong></td></tr><tr><td>Kathrin Hanauer (Faculty of Computer Science, University of Vienna); Monika Henzinger (Institute of Science and Technology Austria (ISTA)); Robin M眉nk (Technical University of Munich); Harald R盲cke (Technical University of Munich); Maximilian V枚tsch (Faculty of Computer Science, UniVie Doctoral School Computer Science DoCS, University of Vienna) </td></tr><tr><td><strong>An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut Problem<strong></strong></strong></td></tr><tr><td>Huaiyuan Liu (Harbin Institute of Technology); Xianzhang Liu (Harbin Institute of Technology); Donghua Yang (Harbin Institute of Technology); Hongzhi Wang(Harbin Institute of Technology); Yingchi Long (Harbin Institute of Technology); Mengtong Ji (Harbin Institute of Technology); Dongjing Miao (Harbin Institute of Technology); Zhiyu Liang (Harbin Institute of Technology) </td></tr><tr><td><strong>Dense Subgraph Discovery Meets Strong Triadic Closure<strong></strong></strong></td></tr><tr><td>Chamalee Wickrama Arachchi(University of Helsinki); Iiro Kumpulainen(University of Helsinki); Nikolaj Tatti (HIIT, University of Helsinki) </td></tr><tr><td><strong>Efficient and Effective Anchored Densest Subgraph Search: A Convex-programming based Approach<strong></strong></strong></td></tr><tr><td>Xiaowei Ye (Beijing Institute of Technology); Rong-Hua Li (Key Laboratory of Intelligent Supply Chain Technology, Longgang District, Shenzhen); Beijing Institute of Technology); Lei Liang (Ant Group); Zhizhen Liu (Ant Group); Longlong Lin (Southwest University); Guoren Wang (Beijing Institute of Technology) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 14:00-16:00, Room 121<br>Theme: Urban Data I<br>Session Chair: Auroop R Ganguly (Northeastern University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization<strong></strong></strong></td></tr><tr><td>Fei Liu (City University of Hong Kong); Xi Lin (City University of Hong Kong); Zhenkun Wang (Southern University of Science and Technology); Qingfu Zhang (City University of Hong Kong); Tong Xialiang (Huawei Technologies Ltd.); Mingxuan Yuan (Huawei Technologies Ltd.) </td></tr><tr><td><strong>CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control<strong></strong></strong></td></tr><tr><td>Jingqing Ruan (Institute of Automation, Chinese Academy of Science, Chinese Academy of Sciences); Ziyue Li (EWI gGmbH, Sensetime Research); Hua Wei (Arizona State University); Haoyuan Jiang (Baidu Inc.); Jiaming Lu (Fudan University); Xuantang Xiong (Institute of Automation, Chinese Academy of Science, Chinese Academy of Sciences); Hangyu Mao (Sensetime Research); Rui Zhao (Sensetime Research, Qing Yuan Research Institute, Shanghai Jiao Tong University) </td></tr><tr><td><strong>CrossLight: Offline-to-Online Reinforcement Learning for Cross-City Traffic Signal Control<strong></strong></strong></td></tr><tr><td>Qian Sun (Hong Kong University of Science and Technology); Rui Zha (University of Science and Technology of China); Le Zhang (Baidu Inc.); Jingbo Zhou (Baidu Inc.); Yu Mei (Baidu Inc.); Zhiling Li (Baidu Inc.); Hui Xiong (Hong Kong University of Science and Technology (Guangzhou), Hong Kong University of Science and Technology) </td></tr><tr><td><strong>Online Preference Weight Estimation Algorithm with Vanishing Regret for Car-Hailing in Road Network<strong></strong></strong></td></tr><tr><td>Yucen Gao (Shanghai Jiaotong University); Zhehao Zhu (Shanghai Jiaotong University); Mingqian Ma (Shanghai Jiaotong University); Fei Gao (Didi Global Inc.); Hui Gao (Didi Global Inc.); Yangguang Shi (Shandong University); Xiaofeng Gao (Shanghai Jiao Tong University) </td></tr><tr><td><strong>Rethinking Order Dispatching in Online Ride-Hailing Platforms<strong></strong></strong></td></tr><tr><td>Zhaoxing Yang (Shanghai Jiao Tong University); Haiming Jin (Shanghai Jiao Tong University); Guiyun Fan (Shanghai Jiao Tong University); Min Lu (Didi Chuxing); Yiran Liu (Didi Chuxing); Xinlang Yue (Didi Chuxing); Hao Pan (Didi Chuxing); Zhe Xu (Didi Chuxing); Guobin Wu (Didi Chuxing); Qun Li (Didi Chuxing); Xiaotong Wang (Didi Chuxing); Jiecheng Guo (Didi Chuxing) </td></tr><tr><td><strong>STONE: A Spatio-temporal OOD Learning Framework Kills Both Spatial and Temporal Shifts<strong></strong></strong></td></tr><tr><td>Binwu Wang (University of Science and Technology of China); Jiaming Ma(University of Science and Technology of China); Pengkun Wang (Suzhou Institute for Advanced Research, University of Science and Technology of China); Xu Wang (Suzhou Institute for Advanced Research, University of Science and Technology of China); Yudong Zhang (University of Science and Technology of China); Zhengyang Zhou(Suzhou Institute for Advanced Research, University of Science and Technology of China); Yang Wang(University of Science and Technology of China) </td></tr><tr><td><strong>UniST: A Prompt-empowered Universal Model for Urban Spatio-temporal Prediction<strong></strong></strong></td></tr><tr><td>Yuan Yuan (Department of Electronic Engineering, BNRist, Tsinghua University); Jingtao Ding (Department of Electronic Engineering, BNRist, Tsinghua University); Jie Feng (Department of Electronic Engineering, BNRist, Tsinghua University); Depeng Jin (Department of Electronic Engineering, BNRist, Tsinghua University); Yong Li (Department of Electronic Engineering, BNRist, Tsinghua University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 14:00-16:00, Room 122<br>Theme: Temporal Graphs I </strong><br><strong>Session Chair: Bijaya Adhikari (University of Iowa)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline<strong></strong></strong></td></tr><tr><td>Guangming Sheng (The University of Hong Kong); Junwei Su (The University of Hong Kong); Chao Huang (The University of Hong Kong); Chuan Wu (The University of Hong Kong) </td></tr><tr><td><strong>Scalable Temporal Motif Densest Subnetwork Discovery<strong></strong></strong></td></tr><tr><td>Ilie Sarpe (KTH Royal Institute of Technology); Fabio Vandin (University of Padova); Aristides Gionis (KTH Royal Institute of Technology) </td></tr><tr><td><strong>Representation Learning of Temporal Graphs with Structural Roles<strong></strong></strong></td></tr><tr><td>Huaming Du (School of Business Administration, Southwestern University of Finance and Economics); Long Shi (Financial Intelligence and Financial Engineering Key Laboratory, Southwestern University of Finance and Economics); Xingyan Chen (Financial Intelligence and Financial Engineering Key Laboratory, Southwestern University of Finance and Economics); Yu Zhao (Financial Intelligence and Financial Engineering Key Laboratory, Southwestern University of Finance and Economics); Hegui Zhang (School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics); Carl Yang (Department of Computer Science, Emory University); Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University, SKLSDE, School of Computer Science, Beihang University); Gang Kou (Xiangjiang Laboratory, School of Business Administration, Southwestern University of Finance and Economics) </td></tr><tr><td><strong>MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning<strong></strong></strong></td></tr><tr><td>Shuo Ji (CCSE Lab, Beihang University); Mingzhe Liu (CCSE Lab, Beihang University); Leilei Sun (CCSE Lab, Beihang University, Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University); Chuanren Liu (University of Tennessee); Tongyu Zhu (CCSE Lab, Beihang University, Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University) </td></tr><tr><td><strong>Making Temporal Betweenness Computation Faster and Restless<strong></strong></strong></td></tr><tr><td>Filippo Brunelli (European Commission — JRC); Pierluigi Crescenzi (Gran Sasso Science Institute); Laurent Viennot (INRIA, DI ENS) </td></tr><tr><td><strong>Latent Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model<strong></strong></strong></td></tr><tr><td>Yuxing Tian (International Digital Economy Academy, IDEA Research); Aiwen Jiang (Jiangxi Normal University); Qi Huang (Jiangxi Normal University); Jian Guo (International Digital Economy Academy, IDEA Research); Yiyan Qi (International Digital Economy Academy, IDEA Research) </td></tr><tr><td><strong>Topology-monitorable Contrastive Learning on Dynamic Graphs<strong></strong></strong></td></tr><tr><td>Zulun Zhu (Nanyang Technological University); Kai Wang (Nanyang Technological University); Haoyu Liu (Nanyang Technological University); Jintang Li (Sun Yat-Sen University); Siqiang Luo (Nanyang Technological University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 14:00-16:00, Room 129-130<br>Theme: Anomaly Detection I </strong><br><strong>Session Chair: David Anastasiu (Santa Clara University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning<strong></strong></strong></td></tr><tr><td>Jongha Lee (KAIST); Sunwoo Kim (KAIST); Kijung Shin (KAIST) </td></tr><tr><td><strong>Graph Anomaly Detection with Few Labels: A Data-Centric Approach<strong></strong></strong></td></tr><tr><td>Xiaoxiao Ma (School of Computing, Macquarie University); Ruikun Li (Business School, The University of Sydney); Fanzhen Liu (School of Computing, Macquarie University); Kaize Ding (Department of Statistics and Data Science, Northwestern University); Jian Yang (School of Computing, Macquarie University); Jia Wu (School of Computing, Macquarie University) </td></tr><tr><td><strong>Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-level Anomaly Detection<strong></strong></strong></td></tr><tr><td>Chunjing Xiao (Henan University); Shikang Pang (Henan University); Wenxin Tai (University of Electronic Science and Technology of China); Yanlong Huang (University of Electronic Science and Technology of China); Goce Trajcevski (Iowa State University); Fan Zhou (University of Electronic Science and Technology of China) </td></tr><tr><td><strong>Fast Unsupervised Deep Outlier Model Selection with Hypernetworks<strong></strong></strong></td></tr><tr><td>Xueying Ding (Carnegie Mellon University); Yue Zhao (University of Southern California); Leman Akoglu (Carnegie Mellon University) </td></tr><tr><td><strong>ReCDA: Concept Drift Adaptation with Representation Enhancement for Network Intrusion Detection<strong></strong></strong></td></tr><tr><td>Shuo Yang (The University of Hong Kong); Xinran Zheng (Tsinghua University); Jinze Li (The University of Hong Kong); Jinfeng Xu (The University of Hong Kong); Xingjun Wang (Tsinghua University); Edith C. H. Ngai (The University of Hong Kong) </td></tr><tr><td><strong>SEBot: Structural Entropy Guided Multi-View Contrastive learning for Social Bot Detection<strong></strong></strong></td></tr><tr><td>Yingguang Yang (University of Science and Technology of China); Qi Wu (University of Science and Technology of China); Buyun He (University of Science and Technology of China); Hao Peng (Beihang University); Renyu Yang (Beihang University); Zhifeng Hao (Shantou University); Yong Liao (University of Science and Technology of China) </td></tr><tr><td><strong>EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy<strong></strong></strong></td></tr><tr><td>Yihong Huang (East China Normal University); Yuang Zhang (East China Normal University); Liping Wang (East China Normal University); Fan Zhang (Guangzhou University); Xuemin Lin (Shanghai Jiaotong University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 14:00-16:00, Room 131-132<br>Theme: Causal inference & Discovery </strong><br><strong>Session Chair: Hong Yu (UMass Lowell)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Counterfactual Generative Models for Time-Varying Treatments<strong></strong></strong></td></tr><tr><td>Shenghao Wu (Carnegie Mellon University); Wenbin Zhou (Carnegie Mellon University); Minshuo Chen (Princeton University); Shixiang Zhu (Carnegie Mellon University) </td></tr><tr><td><strong>CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation<strong></strong></strong></td></tr><tr><td>Fan Wang (College of Computer Science and Technology, Zhejiang University); Chaochao Chen (College of Computer Science and Technology, Zhejiang University); Weiming Liu (College of Computer Science and Technology, Zhejiang University); Tianhao Fan (College of Computer Science and Technology, China University of Petroleum (East China)); Xinting Liao (College of Computer Science and Technology, Zhejiang University); Yanchao Tan (College of Computer and Data Science/College of Software, Fuzhou University); Lianyong Qi (College of Computer Science and Technology, China University of Petroleum (East China)); Xiaolin Zheng (College of Computer Science and Technology, Zhejiang University) </td></tr><tr><td><strong>CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect<strong></strong></strong></td></tr><tr><td>Jiehui Zhou (State Key Lab of CAD&CG, Zhejiang University, DAMO Academy, Alibaba Group); Linxiao Yang (DAMO Academy, Alibaba Group); Xingyu Liu (State Key Lab of CAD&CG, Zhejiang University); Xinyue Gu (DAMO Academy, Alibaba Group); Liang Sun (DAMO Academy, Alibaba Group); Wei Chen (State Key Lab of CAD&CG, Zhejiang University) </td></tr><tr><td><strong>Conformal Counterfactual Inference under Hidden Confounding<strong></strong></strong></td></tr><tr><td>Zonghao Chen (University College London); Ruocheng Guo (Bytedance Research); Jean-Francois Ton (Bytedance Research); Yang Liu (Bytedance Research) </td></tr><tr><td><strong>Learning Causal Networks from Episodic Data<strong></strong></strong></td></tr><tr><td>Osman Mian (CISPA Helmholtz Center for Information Security); Sarah Mameche (CISPA Helmholtz Center for Information Security); Jilles Vreeken (CISPA Helmholtz Center for Information Security) </td></tr><tr><td><strong>Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior<strong></strong></strong></td></tr><tr><td>Pingchuan Ma (Hong Kong University of Science and Technology); Rui Ding (Microsoft Research); Qiang Fu (Microsoft); Jiaru Zhang (Shanghai Jiao Tong University); Shuai Wang (Hong Kong University of Science and Technology); Shi Han (Microsoft Research Asia); Dongmei Zhang (Microsoft) </td></tr><tr><td><strong>Causal Estimation of Exposure Shifts with Neural Networks and an Application to Inform Air Quality Standards in the US<strong></strong></strong></td></tr><tr><td>Mauricio Tec (Harvard University); Kevin Josey (Colorado School of Public Health); Oladimeji Mudele (Harvard University); Francesca Dominici (Harvard University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 14:00-16:00, Room 133<br>Theme: Federated Learning I </strong><br><strong>Session Chair: Jiayu Zhou (University of Michigan)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation<strong></strong></strong></td></tr><tr><td>Tong Xia (University of Cambridge); Abhirup Ghosh (University of Birmingham, University of Cambridge); Xinchi Qiu (University of Cambridge); Cecilia Mascolo (University of Cambridge) </td></tr><tr><td><strong>FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation<strong></strong></strong></td></tr><tr><td>Gang Yan (Binghamton University); Hao Wang (Stevens Institute of Technology); Xu Yuan (University of Delaware); Jian Li (Stony Brook University) </td></tr><tr><td><strong>FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning<strong></strong></strong></td></tr><tr><td>Zihui Wang (Xiamen University); Zheng Wang (Xiamen University); Lingjuan Lyu (Sony Research); Zhaopeng Peng (Xiamen University); Zhicheng Yang (Xiamen University); Chenglu Wen (Xiamen University); Rongshan Yu (National University of Singapore); Cheng Wang (Xiamen University); Xiaoliang Fan (Xiamen University) </td></tr><tr><td><strong>Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning<strong></strong></strong></td></tr><tr><td>Jiayuan Zhang (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University); Xuefeng Liu (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Zhongguancun Laboratory); Zhang Yukang (Shenzhen International Graduate School, Tsinghua University); Guogang Zhu (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University); Jianwei Niu (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Zhongguancun Laboratory); Shaojie Tang (Jindal School of Management, The University of Texas at Dallas) </td></tr><tr><td><strong>CASA: Clustered Federated Learning with Asynchronous Clients<strong></strong></strong></td></tr><tr><td>Boyi Liu (Beihang University); Yiming Ma (Beihang University); Zimu Zhou (City University of Hong Kong); Yexuan Shi (Beihang University); Shuyuan Li (Beihang University); Yongxin Tong (Beihang University) </td></tr><tr><td><strong>FLAIM: AIM-based Synthetic Data Generation in the Federated Setting<strong></strong></strong></td></tr><tr><td>Samuel Maddock (University of Warwick); Graham Cormode (Meta AI, University of Warwick); Carsten Maple (University of Warwick) </td></tr><tr><td><strong>FedNLR: Federated Learning with Neuron-wise Learning Rates<strong></strong></strong></td></tr><tr><td>Haozhao Wang (School of Computer Science and Technology, Huazhong University of Science and Technology); Peirong Zheng (Hong Kong Polytechnic University); Xingshuo Han (Nanyang Technological University); Wenchao Xu (The Hong Kong Polytechnic University); Ruixuan Li (School of Computer Science and Technology, Huazhong University of Science and Technology); Tianwei Zhang (Nanyang Technological University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 14:00-16:00, Room 134<br>Theme: Transfer Learning & OOD </strong><br><strong>Session Chair: Feng Chen (UT Dallas)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Towards Test Time Adaptation via Calibrated Entropy Minimization<strong></strong></strong></td></tr><tr><td>Hao Yang (National University of Defense Technology); Min Wang (National University of Defense Technology); Jinshen Jiang (National University of Defense Technology); Yun Zhou (National University of Defense Technology) </td></tr><tr><td><strong>Fast and Accurate Domain Adaptation for Irregular Tensor Decomposition<strong></strong></strong></td></tr><tr><td>Junghun Kim (Seoul National University); Ka Hyun Park(Seoul National University); Jun-Gi Jang(University of Illinois at Urbana-Champaign); U Kang (Seoul National University) </td></tr><tr><td><strong>POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning<strong></strong></strong></td></tr><tr><td>Junxiang Wang (NEC Labs America); Guangji Bai (Emory University); Wei Cheng (NEC Labs America); Zhengzhang Chen (NEC Labs America); Liang Zhao (Emory University); Haifeng Chen (NEC Labs America) </td></tr><tr><td><strong>Practical Single Domain Generalization via Training-time and Test-time Learning<strong></strong></strong></td></tr><tr><td>Shuai Yang (School of Information and Artificial Intelligence, Anhui Agricultural University); Zhen Zhang (School of Information and Artificial Intelligence, Anhui Agricultural University); Lichuan Gu (School of Information and Artificial Intelligence, Anhui Agricultural University) </td></tr><tr><td><strong>Model-Agnostic Random Weighting for Out-of-Distribution Generalization<strong></strong></strong></td></tr><tr><td>Yue He (Tsinghua University); Pengfei Tian (Tsinghua University); Renzhe Xu (Tsinghua University); Xinwei Shen (ETH Z眉rich); Xingxuan Zhang (Tsinghua University); Peng Cui (Tsinghua University) </td></tr><tr><td><strong>A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction<strong></strong></strong></td></tr><tr><td>Jiahui Gong (Tsinghua University); Jingtao Ding (Tsinghua University); Fanjin Meng (Tsinghua University); Guilong Chen (Honor Device Co., Ltd.); Hong Chen (Honor Device Co., Ltd.); Shen Zhao (Honor Device Co., Ltd.); Haisheng Lu (Honor Device Co., Ltd.); Yong Li (Tsinghua University) </td></tr><tr><td><strong>Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition<strong></strong></strong></td></tr><tr><td>Junru Zhang (Zhejiang University); Lang Feng (Zhejiang University); Zhidan Liu (Shenzhen University); Yuhan Wu (Zhejiang University); Yang He (Zhejiang University); Yabo Dong (Zhejiang University); Duanqing Xu (Zhejiang University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 16:30-18:30, Room 116<br>Theme: LLMs + Semantics<br>Session Chair: Wenqi Fan (The Hong Kong Polytechnic University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>RecExplainer: Aligning Large Language Models for Explaining Recommendation Models</strong></td></tr><tr><td>Yuxuan Lei (University of Science and Technology of China); Jianxun Lian (Microsoft Research Asia); Jing Yao (Microsoft Research Asia); Xu Huang (University of Science and Technology of China); Defu Lian (University of Science and Technology of China); Xing Xie (Microsoft Research Asia)</td></tr><tr><td><strong>Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation</strong></td></tr><tr><td>Xinyu Lin (National University of Singapore); Wenjie Wang (National University of Singapore); Yongqi Li (The Hong Kong Polytechnic University); Fuli Feng (University of Science and Technology of China); See-Kiong Ng (National University of Singapore); Tat-Seng Chua (National University of Singapore)</td></tr><tr><td><strong>CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation</strong></td></tr><tr><td>Junda Wu (University of California San Diego); Cheng-Chun Chang (Columbia University); Tong Yu (Adobe Research); Zhankui He (University of California San Diego); Jianing Wang (University of California San Diego); Yupeng Hou (University of California San Diego); Julian McAuley (University of California San Diego)</td></tr><tr><td><strong>Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System</strong></td></tr><tr><td>Sein Kim (Korea Advanced Institute of Science and Technology); Hongseok Kang (Korea Advanced Institute of Science and Technology); Seungyoon Choi (Korea Advanced Institute of Science and Technology); Donghyun Kim (NAVER Corporation); MinChul Yang (NAVER Corporation); Chanyoung Park (Korea Advanced Institute of Science and Technology)</td></tr><tr><td><strong>DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation</strong></td></tr><tr><td>Kounianhua Du (Shanghai Jiao Tong University); Jizheng Chen (Shanghai Jiaotong University); Jianghao Lin (Shanghai Jiaotong University); Yunjia Xi (Shanghai Jiaotong University); Hangyu Wang (Shanghai Jiao Tong University); Xinyi Dai (Huawei Noah’s Ark Lab); Bo Chen (Huawei Noah’s Ark Lab); Ruiming Tang (Huawei Noah’s Ark Lab); Weinan Zhang (Shanghai Jiao Tong University)</td></tr><tr><td><strong>Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning</strong></td></tr><tr><td>Xiao Han (City University of Hong Kong); Chen Zhu (Career Science Lab, BOSS Zhipin, University of Science and Technology of China); Xiao Hu (Career Science Lab, BOSS Zhipin); Chuan Qin (Career Science Lab, BOSS Zhipin); Xiangyu Zhao (City University of Hong Kong); Hengshu Zhu (Career Science Lab, BOSS Zhipin)</td></tr><tr><td><strong>EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration</strong></td></tr><tr><td>Ye Wang (Zhejiang University); Jiahao Xun (Zhejiang University); Minjie Hong (Zhejiang University); Jieming Zhu (Huawei Noah’s Ark Lab); Tao Jin (Zhejiang University); Lin Wang (Zhejiang University); Haoyuan Li (Zhejiang University); Linjun Li (Zhejiang University); Yan Xia (Zhejiang University); Zhou Zhao (Zhejiang University); Zhenhua Dong (Huawei Noah’s Ark Lab)</td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 16:30-18:30, Room 120<br>Theme: Link Prediction<br>Session Chair: Gabriele Tolomei (Sapienza University of Rome) </strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction<strong></strong></strong></td></tr><tr><td>Ke Cheng (CCSE Lab, Beihang University); Peng Linzhi (CCSE Lab, Beihang University); Junchen Ye (School of Transportation Science and Engineering, Beihang University); Leilei Sun (CCSE Lab, Beihang University); Bowen Du (Zhongguancun Lab, School of Transportation Science and Engineering, Beihang University) </td></tr><tr><td><strong>Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement<strong></strong></strong></td></tr><tr><td>Yakun Wang (Ant Group); Daixin Wang (Ant Group); Hongrui Liu (Ant Group); Binbin Hu (Ant Group); Yingcui Yan (Ant Group); Qiyang Zhang (Ant Group); Zhiqiang Zhang (Ant Group) </td></tr><tr><td><strong>Heuristic Learning with Graph Neural Networks: A Unified Framework for Link Prediction<strong></strong></strong></td></tr><tr><td>Juzheng Zhang (Department of Electronic Engineering, Tsinghua University); Lanning Wei (Department of Electronic Engineering, Tsinghua University); Zhen Xu (Department of Electronic Engineering, Tsinghua University); Quanming Yao (Department of Electronic Engineering, Tsinghua University) </td></tr><tr><td><strong>Conformalized Link Prediction on Graph Neural Networks<strong></strong></strong></td></tr><tr><td>Tianyi Zhao (University of Southern California); Jian Kang (University of Rochester); Lu Cheng (University of Illinois Chicago) </td></tr><tr><td><strong>Causal Subgraph Learning for Generalizable Inductive Relation Prediction<strong></strong></strong></td></tr><tr><td>Mei Li (Global Institute of Future Technology, Shanghai Jiao Tong University, College of Computer Science, Nankai University); Xiaoguang Liu (College of Computer Science, TMCC, SysNet, DISSec, GTIISC, Nankai University); Hua Ji (Department of Computer Science, Civil Aviation University of China); Shuangjia Zheng (Global Institute of Future Technology, Shanghai Jiao Tong University) </td></tr><tr><td><strong>Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs<strong></strong></strong></td></tr><tr><td>Hewen Wang (National University of Singapore); Renchi Yang (Hong Kong Baptist University); Xiaokui Xiao (National University of Singapore) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 16:30-18:30, Room 121<br>Theme: Spatio-temporal Data<br>Session Chair: Zhe Jiang (University of Florida)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction<strong></strong></strong></td></tr><tr><td>Dazhou Yu (Emory University); Xiaoyun Gong (Emory University); Yun Li (Emory University); Meikang Qiu (Augusta University); Liang Zhao (Emory University) </td></tr><tr><td><strong>MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction<strong></strong></strong></td></tr><tr><td>Li Lin (Southeast University); Zhiqiang Lu (Southeast University); Shuai Wang (Southeast University); Yunhuai Liu (Peking University); Zhiqing Hong (Rutgers University); Haotian Wang (JD Logistics); Shuai Wang (Southeast University) </td></tr><tr><td><strong>Long-Term Vessel Trajectory Imputation with Physics-Guided Diffusion Probabilistic Model<strong></strong></strong></td></tr><tr><td>Zhiwen Zhang (School of Artificial Intelligence, Jilin University); Zipei Fan (School of Artificial Intelligence, Jilin University); Zewu Lv (School of Artificial Intelligence, Jilin University); Xuan Song (School of Artificial Intelligence, Jilin University, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology (SUSTech)); Ryosuke Shibasaki (Research & Development Department, LocationMind Inc.) </td></tr><tr><td><strong>RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data<strong></strong></strong></td></tr><tr><td>Chin-Chia Michael Yeh (Visa Research); Yujie Fan (Visa Research); Xin Dai (Visa Research); Uday Singh Saini (Visa Research); Vivian Lai (Visa Research); Prince Aboagye (Visa Research); Junpeng Wang (Visa Research); Huiyuan Chen (Visa Research); Yan Zheng (Visa Research); Zhongfang Zhuang (Visa Research); Liang Wang (Visa Research); Wei Zhang (Visa Research) </td></tr><tr><td><strong>Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting<strong></strong></strong></td></tr><tr><td>Zheng Dong (Southern University of Science and Technology); Renhe Jiang (The University of Tokyo); Haotian Gao (The University of Tokyo); Hangchen Liu (Southern University of Science and Technology); Jinliang Deng (Hong Kong University of Science and Technology); Qingsong Wen (Squirrel AI); Xuan Song (Jilin University, Southern University of Science and Technology) </td></tr><tr><td><strong>ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation<strong></strong></strong></td></tr><tr><td>Tong Nie (Tongji University); Guoyang Qin (Tongji University); Wei Ma (The Hong Kong Polytechnic University); Yuewen Mei (Tongji University); Jian Sun (Tongji University) </td></tr><tr><td><strong>Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks<strong></strong></strong></td></tr><tr><td>Wenying Duan (Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Nanchang University); Tianxiang Fang (Nanchang University); Hong Rao (School of Software, Nanchang University); Xiaoxi He (Faculty of Science and Technology, University of Macau) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 16:30-18:30, Room 122<br>Theme: Graph Transformers </strong><br><strong>Session Chair: Yiu-ming Cheung (Hong Kong Baptist University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>A Novel Prompt Tuning for Graph Transformers: Tailoring Prompts to Graph Topologies<strong></strong></strong></td></tr><tr><td>Jingchao Wang (Guangdong University of Technology); Zhengnan Deng (Guangdong University of Technology); Tongxu Lin (Beijing University of Posts and Telecommunications); Wenyuan Li (Guangdong University of Technology); Shaobin Ling (South China Normal University) </td></tr><tr><td><strong>PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer<strong></strong></strong></td></tr><tr><td>Jiahong Ma (Renmin University of China); Mingguo He (Renmin University of China); Zhewei Wei (Renmin University of China) </td></tr><tr><td><strong>Graph Mamba: Towards Learning on Graphs with State Space Models<strong></strong></strong></td></tr><tr><td>Ali Behrouz (Cornell University); Farnoosh Hashemi (Cornell University) </td></tr><tr><td><strong>Understanding Inter-Session Intentions via Complex Logical Reasoning<strong></strong></strong></td></tr><tr><td>Jiaxin Bai (Department of CSE, HKUST); Chen Luo (Amazon.com Inc); Zheng Li (Amazon.com Inc); Qingyu Yin (Amazon.com Inc); Yangqiu Song (Department of CSE, HKUST) </td></tr><tr><td><strong>LPFormer: An Adaptive Graph Transformer for Link Prediction<strong></strong></strong></td></tr><tr><td>Harry Shomer (Michigan State University); Yao Ma (Rensselaer Polytechnic Institute); Mao Haitao (Michigan State University); Juanhui Li (Michigan State University); Bo Wu (Colorado School of Mines); Jiliang Tang (Michigan State University) </td></tr><tr><td><strong>Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection<strong></strong></strong></td></tr><tr><td>Junyou Zhu (School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University, Potsdam Institute for Climate Impact Research); Chao Gao (School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University); Ze Yin (College of Computer Science and Electronic Engineering, Hunan University); Xianghua Li (School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University); Juergen Kurths (Potsdam Institute for Climate Impact Research, Department of Physics, Humboldt-Universit盲t zu Berlin) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 16:30-18:30, Room 129-130<br>Theme: Graphs+LLMs & RAG </strong><br><strong>Session Chair: Latifur Khan (UT Dallas)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>HiGPT: Heterogeneous Graph Language Model<strong></strong></strong></td></tr><tr><td>Jiabin Tang (University of Hong Kong); Yuhao Yang (University of Hong Kong); Wei Wei (University of Hong Kong); Lei Shi (Baidu); Long Xia (Baidu Inc.); Dawei Yin (Baidu); Chao Huang (University of Hong Kong) </td></tr><tr><td><strong>GraphWiz: An Instruction-Following Language Model for Graph Problems<strong></strong></strong></td></tr><tr><td>Nuo Chen (CSE, Hong Kong University of Science and Technology (Guangzhou)); Yuhan Li (Hong Kong University of Science and Technology (Guangzhou)); Jianheng Tang (Hong Kong University of Science and Technology (Guangzhou)); Jia Li (The Hong Kong University of Science and Technology (Guangzhou)) </td></tr><tr><td><strong>GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models<strong></strong></strong></td></tr><tr><td>Yi Fang (SFSC of AI and DL, New York University (Shanghai)); Dongzhe Fan (SFSC of AI and DL, New York University (Shanghai)); Daochen Zha (Rice University); Qiaoyu Tan (SFSC of AI and DL, New York University (Shanghai)) </td></tr><tr><td><strong>Retrieval-Augmented Hypergraph for Multimodal Social Media Popularity Prediction<strong></strong></strong></td></tr><tr><td>Zhangtao Cheng (University of Electronic Science and Technology of China); Jienan Zhang (University of Electronic Science and Technology of China); Xovee Xu (University of Electronic Science and Technology of China); Goce Trajcevski (Iowa State University); Ting Zhong (University of Electronic Science and Technology of China, Kash Institute of Electronics and Information Industry); Fan Zhou (University of Electronic Science and Technology of China, Kash Institute of Electronics and Information Industry) </td></tr><tr><td><strong>FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering<strong></strong></strong></td></tr><tr><td>Tianchi Cai (Ant Group); Zhiwen Tan (Alibaba Group); Xierui Song (Tsinghua University); Tao Sun (Ant Group); Jiyan Jiang (Tsinghua University); Yunqi Xu (Ant Group); Yinger Zhang (Zhejiang University); Jinjie Gu (Nanjing University) </td></tr><tr><td><strong>Killing Two Birds with One Stone: Cross-modal Reinforced Prompting for Graph and Language Tasks<strong></strong></strong></td></tr><tr><td>Wenyuan Jiang (School of Computer Science and Technology, University of Science and Technology of China); Wenwei Wu (Thrust of Financial Technology, The Hong Kong University of Science and Technology (Guangzhou)); Le Zhang (Baidu Research, Baidu Inc.); Zixuan Yuan (Thrust of Financial Technology, The Hong Kong University of Science and Technology (Guangzhou)); Jian Xiang (Thrust of Financial Technology, The Hong Kong University of Science and Technology (GuangZhou)); Jingbo Zhou (Baidu Research, Baidu Inc.); Hui Xiong (Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Department of Computer Science and Engineering, The Hong Kong University of Science and Technology) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 16:30-18:30, Room 131-132<br>Theme: Fairness </strong><br><strong>Session Chair: Carlos Castillo (ICREA and Univ Pompeu Fabra)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Promoting Fairness and Priority in Selecting k-Winners Using IRV<strong></strong></strong></td></tr><tr><td>Md Mouinul Islam (CS, NJIT); Soroush Vahidi (CS, NJIT); Baruch Schieber (CS, NJIT); Senjuti Basu Roy (CS, NJIT) </td></tr><tr><td><strong>Fairness in Streaming Submodular Maximization Subject to a Knapsack Constraint<strong></strong></strong></td></tr><tr><td>Shuang Cui (School of Computer Science and Technology, University of Science and Technology of China); Kai Han (School of Computer Science and Technology, Soochow University); Shaojie Tang (The University of Texas at Dallas); Feng Li (Shandong University, Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences)); Jun Luo (Nanyang Technological University) </td></tr><tr><td><strong>AIM: Attributing, Interpreting, Mitigating Data Unfairness<strong></strong></strong></td></tr><tr><td>Zhining Liu (University of Illinois Urbana-Champaign); Ruizhong Qiu (University of Illinois Urbana-Champaign); Zhichen Zeng (University of Illinois Urbana-Champaign); Yada Zhu (IBM Research); Hendrik Hamann (IBM Research); Hanghang Tong (University of Illinois Urbana-Champaign) </td></tr><tr><td><strong>Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously<strong></strong></strong></td></tr><tr><td>Chen Zhao (Baylor University); Kai Jiang (The University of Texas at Dallas); Xintao Wu (University of Arkansas); Haoliang Wang (The University of Texas at Dallas); Latifur Khan (The University of Texas at Dallas); Christan Grant (University of Florida); Feng Chen (The University of Texas, Dallas) </td></tr><tr><td><strong>Fair Column Subset Selection<strong></strong></strong></td></tr><tr><td>Antonis Matakos (Aalto University); Bruno Ordozgoiti (Unaffiliated); Suhas Thejaswi (Max Planck Institute for Software Systems) </td></tr><tr><td><strong>Using Self-supervised Learning Can Improve Model Fairness<strong></strong></strong></td></tr><tr><td>Sofia Yfantidou (Aristotle University of Thessaloniki); Dimitris Spathis (Nokia Bell Labs); Marios Constantinides (Nokia Bell Labs); Athena Vakali (Aristotle University of Thessaloniki); Daniele Quercia (Nokia Bell Labs); Fahim Kawsar (Nokia Bell Labs) </td></tr><tr><td><strong>Neural Collapse Inspired Debiased Representation Learning for Min-max Fairness<strong></strong></strong></td></tr><tr><td>Shenyu Lu (Purdue University); Junyi Chai (Purdue University); Xiaoqian Wang (Purdue University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 16:30-18:30, Room 133<br>Theme: Reinforcement Learning </strong><br><strong>Session Chair: Mingfei Sun (Univerisity of Manchester)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Policy-Based Bayesian Active Causal Discovery with Deep Reinforcement Learning<strong></strong></strong></td></tr><tr><td>Heyang Gao (Gaoling School of Artificial Intelligence, Renmin University of China); Zexu Sun (Gaoling School of Artificial Intelligence, Renmin University of China); Hao Yang (Gaoling School of Artificial Intelligence, Renmin University of China); Xu Chen (Gaoling School of Artificial Intelligence, Renmin University of China) </td></tr><tr><td><strong>Offline Imitation Learning with Model-based Reverse Augmentation<strong></strong></strong></td></tr><tr><td>Jie-Jing Shao (National Key Laboratory for Novel Software Technology, Nanjing University); Haosen Shi (National Key Laboratory for Novel Software Technology, Nanjing University); Lan-Zhe Guo (National Key Laboratory for Novel Software Technology, School of Intelligence Science and Technology, Nanjing University); Yu-Feng Li (National Key Laboratory for Novel Software Technology, School of Artificial Intelligence, Nanjing University) </td></tr><tr><td><strong>DyPS: Dynamic Parameter Sharing in Multi-Agent Reinforcement Learning for Spatio-Temporal Resource Allocation<strong></strong></strong></td></tr><tr><td>Jingwei Wang (Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University); Qianyue Hao (Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University); Wenzhen Huang (Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University); Xiaochen Fan (Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University); Zhentao Tang (Huawei Noah’s Ark Lab); Bin Wang (Huawei Noah’s Ark Lab); Jianye Hao (Huawei Noah’s Ark Lab, Tianjin University); Yong Li (Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University) </td></tr><tr><td><strong>STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning<strong></strong></strong></td></tr><tr><td>Wei Shao (Data61, CSIRO); Yufan Kang (RMIT University); Ziyan Peng (Xidian University); Xiao Xiao (Xidian University); Lei Wang (Zhejiang University); Yuhui Yang (Xidian University); Flora D. Salim (University of New South Wales) </td></tr><tr><td><strong>Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing<strong></strong></strong></td></tr><tr><td>Xinbo Zhao (Binghamton University); Yingxue Zhang (Binghamton University); Xin Zhang (San Diego State University); Yu Yang (Lehigh University); Yiqun Xie (University of Maryland, College Park); Yanhua Li (Worcester Polytechnic Institute); Jun Luo (Logistics and Supply Chain MultiTech R&D Centre) </td></tr><tr><td><strong>Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks<strong></strong></strong></td></tr><tr><td>Feiyang Xu (Polytechnic Institute, Zhejiang University, State Key Laboratory of Blockchain and Security, Zhejiang University); Shunyu Liu (State Key Laboratory of Blockchain and Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security); Yunpeng Qing (State Key Laboratory of Blockchain and Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security); Yihe Zhou (State Key Laboratory of Blockchain and Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security); Yuwen Wang (State Key Laboratory of Blockchain and Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security); Mingli Song (State Key Laboratory of Blockchain and Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Tuesday, August 27 16:30-18:30, Room 134<br>Theme: Deep Learning I </strong><br><strong>Session Chair: Kyuseok Shim (Seoul National University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Neural Manifold Operators for Learning the Evolution of Physical Dynamics<strong></strong></strong></td></tr><tr><td>Hao Wu (Tencent TEG); Kangyu Weng (Xingjian College, Tsinghua University); Shuyi Zhou (Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University); Xiaomeng Huang (Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University); Wei Xiong (Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University) </td></tr><tr><td><strong>Can a Deep Learning Model be a Sure Bet for Tabular Prediction?<strong></strong></strong></td></tr><tr><td>Jintai Chen (Univ. of Illinois Urbana-Champaign); Jiahuan Yan (Zhejiang University); Qiyuan Chen (Zhejiang University); Danny Chen (University of Notre Dame); Jian Wu (Zhejiang University); Jimeng Sun (Univ. of Illinois Urbana-Champaign) </td></tr><tr><td><strong>Hierarchical Linear Symbolized Tree-Structured Neural Processes<strong></strong></strong></td></tr><tr><td>Jin yang Tai (School of Computer Engineering and Science, Shanghai University); Yi ke Guo (Department of Computer Science and Engineering, Hong Kong University of Science and Technology) </td></tr><tr><td><strong>Layer-Wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning<strong></strong></strong></td></tr><tr><td>Sunwoo Lee (Department of Computer Engineering, Inha University) </td></tr><tr><td><strong>Improving Robustness of Hyperbolic Neural Networks by Lipschitz Analysis<strong></strong></strong></td></tr><tr><td>Yuekang Li (Applied Mathematics and Computational Sciences, DNAS, Duke Kunshan University); Yidan Mao (Applied Mathematics and Computational Sciences, DNAS, Duke Kunshan University); Yifei Yang (Electronic Information School, Wuhan University); Dongmian Zou (Zu Chongzhi Center and Data Science Research Center, DNAS, Duke Kunshan University) </td></tr><tr><td><strong>DipDNN: Preserving Inverse Consistency and Approximation Efficiency for Invertible Learning<strong></strong></strong></td></tr><tr><td>Jingyi Yuan (EECS, Arizona State University); Yang Weng (EECS, Arizona State University); Erik Blasch (Air Force Research Laboratory) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 11:00-13:00, Room 116<br>Theme: Recommendations with Graphs</strong><br><strong>Session Chair: Bryan Perozzi (Google Research)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning</strong></td></tr><tr><td>Jiakai Tang (Gaoling School of Artificial Intelligence, Renmin University of China); Sunhao Dai (Gaoling School of Artificial Intelligence, Renmin University of China); Zexu Sun (Gaoling School of Artificial Intelligence, Renmin University of China); Xu Chen (Gaoling School of Artificial Intelligence, Renmin University of China); Jun Xu (Gaoling School of Artificial Intelligence, Renmin University of China); Wenhui Yu (Kuaishou Technology); Lantao Hu (Kuaishou Technology); Peng Jiang (Kuaishou Technology); Han Li (Kuaishou Technology)</td></tr><tr><td><strong>GPFedRec: Graph-Guided Personalization for Federated Recommendation</strong></td></tr><tr><td>Chunxu Zhang (College of Computer Science and Technology, Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University); Guodong Long (Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney); Tianyi Zhou (Computer Science and UMIACS, University of Maryland); Zijian Zhang (College of Computer Science and Technology, Jilin University, City University of Hong Kong); Peng Yan (Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney); Bo Yang (College of Computer Science and Technology, Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University)</td></tr><tr><td><strong>How Powerful is Graph Filtering for Recommendation</strong></td></tr><tr><td>Shaowen Peng (Nara Institute of Science and Technology); Xin Liu (National Institute of Advanced Industrial Science and Technology (AIST)); Kazunari Sugiyama (Osaka Seikei University); Tsunenori Mine (Kyushu University)</td></tr><tr><td><strong>Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering</strong></td></tr><tr><td>Yihong Wu (Universit茅 de Montr茅al); Le Zhang (Mila – Quebec AI Institute); Fengran Mo (Universit茅 de Montr茅al); Tianyu Zhu (MIIT Key Laboratory of Data Intelligence and Management, Beihang University); Weizhi Ma (Institute for AI Industry Research (AIR), Tsinghua University); Jian-Yun Nie (Universit茅 de Montr茅al)</td></tr><tr><td><strong>Graph Bottlenecked Social Recommendation</strong></td></tr><tr><td>Yonghui Yang (Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology); Le Wu (Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology); Zihan Wang (Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology); Zhuangzhuang He (Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology); Richang Hong (Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology); Meng Wang (Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology)</td></tr><tr><td><strong>When Box Meets Graph Neural Network in Tag-aware Recommendation</strong></td></tr><tr><td>Fake Lin (University of Science and Technology of China); Ziwei Zhao (University of Science and Technology of China); Xi Zhu (University of Science and Technology of China); Da Zhang (University of Science and Technology of China); Shitian Shen (Alibaba Group); Xueying Li (Alibaba Group); Tong Xu (University of Science and Technology of China); Suojuan Zhang (Army Engineering University of PLA); Enhong Chen (University of Science and Technology of China)</td></tr><tr><td><strong>Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations</strong></td></tr><tr><td>Linxin Guo (Chongqing University); Yaochen Zhu (University of Virginia, Charlottesville); Min Gao (Chongqing University); Yinghui Tao (Chongqing University); Junliang Yu (University of Queensland); Chen Chen (University of Virginia, Charlottesville)</td></tr><tr><td><strong>Customizing Graph Neural Network for CAD Assembly Recommendation</strong></td></tr><tr><td>Fengqi Liang (Beijing University of Post and Telecommunication); Huan Zhao (4Paradigm Inc.); Yuhan Quan (4Paradigm Inc.); Wei Fang (Beijing University of Post and Telecommunication); Chuan Shi (Beijing University of Post and Telecommunication)</td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 11:00-13:00, Room 120<br>Theme: Graphs: Generalization & Adaptation</strong><br><strong>Session Chair: Panagiotis Karras (University of Copenhagen)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective<strong></strong></strong></td></tr><tr><td>Kai Guo (School of Artificial Intelligence, Jilin University); Hongzhi Wen (Department of Computer Science and Engineering, Michigan State University); Wei Jin (Department of Computer Science, Emory University); Yaming Guo (School of Artificial Intelligence, Jilin University); Jiliang Tang (Department of Computer Science and Engineering, Michigan State University); Yi Chang (School of Artificial Intelligence, Jilin University) </td></tr><tr><td><strong>DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization<strong></strong></strong></td></tr><tr><td>Xin Sun (University of Science and Technology of China, NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences); Liang Wang (NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences); Qiang Liu (NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences); Shu Wu (NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences); Zilei Wang (University of Science and Technology of China); Liang Wang (NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences) </td></tr><tr><td><strong>An Energy-centric Framework for Category-free Out-of-distribution Node Detection in Graphs<strong></strong></strong></td></tr><tr><td>Zheng Gong (Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou)); Ying Sun (Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou)) </td></tr><tr><td><strong>Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization<strong></strong></strong></td></tr><tr><td>Haohui Wang (Virginia Tech); Baoyu Jing (University of Illinois Urbana-Champaign); Kaize Ding (Northwestern University); Yada Zhu (IBM Research); Wei Cheng (NEC Labs America); Si Zhang (Meta); Yonghui Fan (Amazon AGI); Liqing Zhang (Virginia Tech); Dawei Zhou (Virginia Tech) </td></tr><tr><td><strong>ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs<strong></strong></strong></td></tr><tr><td>Yuhan Li (Hong Kong University of Science and Technology); Peisong Wang (Tsinghua University); Zhixun Li (The Chinese University of Hong Kong); Jeffrey Yu (The Chinese University of Hong Kong); Jia Li (The Hong Kong University of Science and Technology) </td></tr><tr><td><strong>Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling<strong></strong></strong></td></tr><tr><td>Tianxiang Zhao (The Pennsylvania State University); Dongsheng Luo (Florida International University); Xiang Zhang (The Pennsylvania State University); Suhang Wang (The Pennsylvania State University) </td></tr><tr><td><strong>Can Modifying Data Address Graph Domain Adaptation?<strong></strong></strong></td></tr><tr><td>Renhong Huang (Zhejiang University, Fudan University); Jiarong Xu (Fudan University); Xin Jiang (Lehigh University); Ruichuan An (Xi’an Jiaotong University); Yang Yang (Zhejiang University) </td></tr><tr><td><strong>Distributional Network of Networks for Modeling Data Heterogeneity<strong></strong></strong></td></tr><tr><td>Jun Wu (University of Illinois at Urbana-Champaign); Jingrui He (University of Illinois at Urbana-Champaign); Hanghang Tong (University of Illinois at Urbana-Champaign) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 11:00-13:00, Room 121<br>Theme: Interpretability & Explainability</strong><br><strong>Session Chair: Auroop R Ganguly (Northeastern University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks<strong></strong></strong></td></tr><tr><td>Yuwen Wang (School of Software Technology, Zhejiang University, State Key Laboratory of Blockchain and Security); Shunyu Liu (State Key Laboratory of Blockchain and Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security); Tongya Zheng (Big Graph Center, School of Computer and Computing Science, Hangzhou City University, College of Computer Science and Technology, Zhejiang University); Kaixuan Chen (State Key Laboratory of Blockchain and Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security); Mingli Song (State Key Laboratory of Blockchain and Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security) </td></tr><tr><td><strong>Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals<strong></strong></strong></td></tr><tr><td>Bardh Prenkaj (Technical University of Munich); Mario Villaiz谩n-Vallelado (University of Valladolid, Telef贸nica Research and Development); Tobias Leemann (University of T眉bingen); Gjergji Kasneci (Technical University of Munich) </td></tr><tr><td><strong>Natural Language Explainable Recommendation with Robustness Enhancement<strong></strong></strong></td></tr><tr><td>Jingsen Zhang (Gaoling School of Artificial Intelligence, Renmin University of China); Jiakai Tang (Gaoling School of Artificial Intelligence, Renmin University of China); Xu Chen (Gaoling School of Artificial Intelligence, Renmin University of China); Wenhui Yu (Kuaishou Technology); Lantao Hu (Kuaishou Technology); Peng Jiang (Kuaishou Technology); Han Li (Kuaishou Technology) </td></tr><tr><td><strong>CAT: Interpretable Concept-based Taylor Additive Models<strong></strong></strong></td></tr><tr><td>Viet Duong (William & Mary); Qiong Wu (AT&T Labs); Zhengyi Zhou (AT&T Labs); Hongjue Zhao (University of Illinois at Urbana-Champaign); Chenxiang Luo (William & Mary); Eric Zavesky (AT&T Labs); Huaxiu Yao (The University of North Carolina at Chapel Hill); Huajie Shao (William & Mary) </td></tr><tr><td><strong>Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance<strong></strong></strong></td></tr><tr><td>Thomas Decker (Ludwig-Maximilians-Universit盲t, Siemens AG); Alexander Koebler (Goethe University Frankfurt, Siemens AG); Michael Lebacher (Siemens AG); Ingo Thon (Siemens AG); Volker Tresp (Ludwig-Maximilians-Universit盲t, Munich Center for Machine Learning); Florian Buettner (German Cancer Research Center, Goethe University Frankfurt) </td></tr><tr><td><strong>CoLiDR: Concept Learning using Aggregated Disentangled Representations<strong></strong></strong></td></tr><tr><td>Sanchit Sinha (University of Virginia); Guangzhi Xiong (University of Virginia); Aidong Zhang (University of Virginia) </td></tr><tr><td><strong>Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks<strong></strong></strong></td></tr><tr><td>Zizhuo Meng (The University of Technology Sydney); Ke Wan (University of Illinois at Urbana-Champaign); Yadong Huang (Zhoushan Academy of Marine Data Science); Zhidong Li (The University of Technology Sydney); Yang Wang (The University of Technology Sydney); Feng Zhou (Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing) </td></tr><tr><td><strong>FaultInsight: Interpreting Hyperscale Data Center Host Faults<strong></strong></strong></td></tr><tr><td>Tingzhu Bi (Peking University); Zhang Yang (ByteDance Inc.); Yicheng Pan (Peking University); Yu Zhang (ByteDance Inc.); Meng Ma (Peking University, Shuanghu Laboratory); Xinrui Jiang (Peking University); Linlin Han (ByteDance Inc.); Feng Wang (ByteDance Inc.); Liu Xian (ByteDance Inc.); Ping Wang (Peking University, Shuanghu Laboratory) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 11:00-13:00, Room 122<br>Theme: NLP</strong><br><strong>Session Chair: Latifur Khan (UT Dallas)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Ontology Enrichment for Effective Fine-grained Entity Typing<strong></strong></strong></td></tr><tr><td>Siru Ouyang (University of Illinois Urbana-Champaign); Jiaxin Huang (Washington University in Saint Louis); Pranav Pillai (University of Illinois Urbana-Champaign); Yunyi Zhang (University of Illinois Urbana-Champaign); Yu Zhang (University of Illinois Urbana-Champaign); Jiawei Han (University of Illinois Urbana-Champaign) </td></tr><tr><td><strong>OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing<strong></strong></strong></td></tr><tr><td>Tanay Komarlu (University of Illinois Urbana-Champaign); Minhao Jiang (University of Illinois Urbana-Champaign); Xuan Wang (Virginia Tech); Jiawei Han (University of Illinois Urbana-Champaign) </td></tr><tr><td><strong>Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations<strong></strong></strong></td></tr><tr><td>Yuanfeng Song (AI Group, WeBank Co., Ltd.); Xuefang Zhao (AI Group, WeBank Co., Ltd.); Raymond Wong (Department of Computer Science and Engineering, Hong Kong University of Science and Technology) </td></tr><tr><td><strong>Hate Speech Detection with Generalizable Target-aware Fairness<strong></strong></strong></td></tr><tr><td>Tong Chen (The University of Queensland); Danny Wang (The University of Queensland); Xurong Liang (The University of Queensland); Marten Risius (The University of Queensland); Gianluca Demartini (The University of Queensland); Hongzhi Yin (The University of Queensland) </td></tr><tr><td><strong>CoMAL: Contrastive Active Learning for Multi-Label Text Classification<strong></strong></strong></td></tr><tr><td>Cheng Peng (State Key Lab of Blockchain and Data Security, Zhejiang University); Haobo Wang (School of Software Technology, Zhejiang University); Ke Chen (State Key Lab of Blockchain and Data Security, Zhejiang University); Lidan Shou (State Key Lab of Blockchain and Data Security, Zhejiang University); Chang Yao (School of Software Technology, Zhejiang University); Runze Wu (Fuxi AI Lab, NetEase Corp.); Gang Chen (State Key Lab of Blockchain and Data Security, Zhejiang University) </td></tr><tr><td><strong>Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning<strong></strong></strong></td></tr><tr><td>Zhijie Nie (CCSE, Beihang University); Richong Zhang (CCSE, Beihang University); Zhangchi Feng (CCSE, Beihang University); Hailang Huang (CCSE, Beihang University); Xudong Liu (CCSE, Beihang University) </td></tr><tr><td><strong>Binder: Hierarchical Concept Representation through Order Embedding of Binary Vectors<strong></strong></strong></td></tr><tr><td>Croix Gyurek (University of Waterloo); Niloy Talukder (Indiana University at Indianapolis); Mohammad Al Hasan (Indiana University at Indianapolis) </td></tr><tr><td><strong>MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning<strong></strong></strong></td></tr><tr><td>Sanchit Sinha (University of Virginia); Yuguang Yue (Amazon AGI); Victor Soto (Amazon AGI); Mayank Kulkarni (Amazon AGI); Jianhua Lu (Amazon AGI); Aidong Zhang (University of Virginia) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 11:00-13:00, Room 129-130<br>Theme: Graph Neural Networks II</strong><br><strong>Session Chair: Yuxuan Liang (Hong Kong University of Science and Technology (Guangzhou))</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>An Efficient Subgraph GNN with Provable Substructure Counting Power<strong></strong></strong></td></tr><tr><td>Zuoyu Yan (Wangxuan Institute of Computer Technology, Peking University); Junru Zhou (Institute for Artificial Intelligence, Peking University); Liangcai Gao (Wangxuan Institute of Computer Technology, Peking University); Zhi Tang (Wangxuan Institute of Computer Technology, Peking University); Muhan Zhang (Beijing Institute for General Artificial Intelligence, Peking University) </td></tr><tr><td><strong>DPHGNN: A Dual Perspective Hypergraph Neural Networks<strong></strong></strong></td></tr><tr><td>Siddhant Saxena (Indian Institute of Technology Delhi); Shounak Ghatak (Indraprastha Institute of Information Technology, Delhi); Raghu Kolla (Meesho); Debashis Mukherjee (Meesho); Tanmoy Chakraborty (Indian Institute of Technology, Delhi) </td></tr><tr><td><strong>Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Broad Physical Dynamics Learning<strong></strong></strong></td></tr><tr><td>Zinan Zheng (Hong Kong University of Science and Technology (Guangzhou)); Yang Liu (The Hong Kong University of Science and Technology, The Hong Kong University of Science and Technology (Guangzhou)); Jia Li (The Hong Kong University of Science and Technology (Guangzhou)); Jianhua Yao (Tencent AI Lab); Yu Rong (Tencent AI Lab) </td></tr><tr><td><strong>A Deep Prediction Framework for Multi-Source Information via Heterogeneous GNN<strong></strong></strong></td></tr><tr><td>Zhen Wu (School of Computer Science and Technology, Soochow University, School of Computer Science and Engineering, Southeast University); Jingya Zhou (School of Computer Science and Technology, Engineering Lab of Big Data and Intelligence of Jiangsu Province, Soochow University, State Key Lab for Novel Software Technology, Nanjing University); Jinghui Zhang (School of Computer Science and Engineering, Southeast University); Ling Liu (School of Computer Science, Georgia Institute of Technology); Chizhou Huang (School of Computer Science and Technology, Soochow University) </td></tr><tr><td><strong>Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification<strong></strong></strong></td></tr><tr><td>Beini Xie (DCST, Tsinghua University); Heng Chang(DCST, Tsinghua University); Ziwei Zhang (DCST, Tsinghua University); Zeyang Zhang (DCST, Tsinghua University); Simin Wu (Lanzhou University); Xin Wang (DCST, BNRist, Tsinghua University); Yuan Meng (DCST, Tsinghua University); Wenwu Zhu (DCST, BNRist, Tsinghua University) </td></tr><tr><td><strong>AGS-GNN: Attribute-guided Sampling for Graph Neural Network<strong></strong></strong></td></tr><tr><td>Siddhartha Shankar Das (Computer Science, Purdue University); S M Ferdous (Pacific Northwest National Laboratory); Mahantesh Halappanavar (Pacific Northwest National Laboratory); Edoardo Serra (Computer Science, Boise State University); Alex Pothen (Computer Science, Purdue University) </td></tr><tr><td><strong>The Snowflake Hypothesis: Training and Powering GNN with One Node One Receptive Field<strong></strong></strong></td></tr><tr><td>Kun Wang (University of Science and Technology of China); Guohao Li (University of Oxford); Shilong Wang (University of Science and Technology of China); Guibin Zhang (International Digital Economy Academy); Kai Wang (National University of Singapore); Yang You (National University of Singapore); Junfeng Fang (University of Science and Technology of China); Xiaojiang Peng (Shenzhen Technology University); Yuxuan Liang (The Hong Kong University of Science and Technology (Guangzhou)); Yang Wang (University of Science and Technology of China) </td></tr><tr><td><strong>TSC: a Simple Two-Sided Constraint against Over-Smoothing<strong></strong></strong></td></tr><tr><td>Furong Peng (Institute of Big Data Science and Industry, Shanxi University, School of Computer and Information Technology, Shanxi University); Kang Liu (Institute of Big Data Science and Industry, Shanxi University, School of Computer and Information Technology, Shanxi University); Xuan Lu (College of Physics and Electronic Engineering, Shanxi University); Yuhua Qian (Institute of Big Data Science and Industry, Shanxi University, School of Computer and Information Technology, Shanxi University); Hongren Yan (Institute of Big Data Science and Industry, Shanxi University, School of Computer and Information Technology, Shanxi University); Chao Ma (HOPERUN Infomation Technology) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 11:00-13:00, Room 131-132<br>Theme: Anomaly Detection II</strong><br><strong>Session Chair: Kenji Yamanishi (University of Tokyo)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Multivariate Log-based Anomaly Detection for Distributed Database<strong></strong></strong></td></tr><tr><td>Lingzhe Zhang (Peking University); Tong Jia (Peking University); Mengxi Jia (Peking University); Ying Li (Peking University); Yong Yang (Peking University); Zhonghai Wu (Peking University) </td></tr><tr><td><strong>Multi-Scale Detection of Anomalous Spatio-Temporal Trajectories in Evolving Trajectory Datasets<strong></strong></strong></td></tr><tr><td>Chenhao Wang (University of Electronic Science and Technology of China); Lisi Chen (University of Electronic Science and Technology of China); Shuo Shang (University of Electronic Science and Technology of China); Christian S. Jensen (Aalborg University); Panos Kalnis (King Abdullah University of Science and Technology) </td></tr><tr><td><strong>SensitiveHUE: Multivariate Time Series Anomaly Detection by Enhancing the Sensitivity to Normal Patterns<strong></strong></strong></td></tr><tr><td>Yuye Feng (Hikvision Research Institute); Wei Zhang (Hikvision Research Institute); Yao Fu (Hikvision Research Institute); Weihao Jiang (Hikvision Research Institute); Jiang Zhu (Hikvision Research Institute); Wenqi Ren (Hikvision Research Institute) </td></tr><tr><td><strong>PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection<strong></strong></strong></td></tr><tr><td>Ronghui Xu (Central South University); Hao Miao (Aalborg University); Senzhang Wang (Central South University); Philip S. Yu (University of Illinois at Chicago); Jianxin Wang (Central South University) </td></tr><tr><td><strong>CutAddPaste: Time Series Anomaly Detection by Exploiting Abnormal Knowledge<strong></strong></strong></td></tr><tr><td>Rui Wang (School of Computer Science and Engineering, Beihang University); Xudong Mou (School of Computer Science and Engineering, Beihang University); Renyu Yang (School of Software, Beihang University); Kai Gao (Institute of Future Cities, The Chinese University of Hong Kong); Pin Liu (School of Information Engineering, China University of Geosciences Beijing); Chongwei Liu (Kuaishou Inc.); Tianyu Wo (School of Software, Beihang University, Zhongguancun Laboratory); Xudong Liu (School of Computer Science and Engineering, Beihang University, Zhongguancun Laboratory) </td></tr><tr><td><strong>PATE: Proximity-Aware Time Series Anomaly Evaluation<strong></strong></strong></td></tr><tr><td>Ramin Ghorbani (Delft University of Technology); Marcel J.T. Reinders (Delft University of Technology); David M.J. Tax (Delft University of Technology) </td></tr><tr><td><strong>Cluster-Wide Task Slowdown Detection in Cloud System<strong></strong></strong></td></tr><tr><td>Feiyi Chen (Zhejiang University, Alibaba Group); Yingying Zhang (Alibaba Group); Lunting Fan (Alibaba Group); Yuxuan Liang (The Hong Kong University of Science and Technology (Guangzhou)); Guansong Pang (Singapore Management University); Qingsong Wen (Squirrel AI); Shuiguang Deng (Zhejiang University) </td></tr><tr><td><strong>Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask<strong></strong></strong></td></tr><tr><td>Jingyu Xiao (Peng Cheng Laboratory, Tsinghua Shenzhen International Graduate School); Zhiyao Xu (Xi’an University of Electronic Science and Technology); Qingsong Zou (Tsinghua Shenzhen International Graduate School, Peng Cheng Laboratory); Qing Li (Peng Cheng Laboratory); Dan Zhao (Peng Cheng Laborotary); Dong Fang (Tencent); Ruoyu Li (Tsinghua Shenzhen International Graduate School); Wenxin Tang (Tsinghua Shenzhen International Graduate School); Kang Li (Tsinghua Shenzhen International Graduate School); Xudong Zuo (Tsinghua Shenzhen International Graduate School); Penghui Hu (Tsinghua University); Yong Jiang (Tsinghua Shenzhen International Graduate School, Peng Cheng Laboratory); Zixuan Weng (Beijing Jiaotong University); Michael R.Lyu (The Chinese University of Hong Kong) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 11:00-13:00, Room 133<br>Theme: Federated Learning II</strong><br><strong>Session Chair: Jiayu Zhou (University of Michigan)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning<strong></strong></strong></td></tr><tr><td>Yidan Xing (Shanghai Jiao Tong University); Zhenzhe Zheng (Shanghai Jiao Tong University); Fan Wu (Shanghai Jiao Tong University) </td></tr><tr><td><strong>Asynchronous Vertical Federated Learning for Kernelized AUC Maximization<strong></strong></strong></td></tr><tr><td>Ke Zhang (Huazhong Agricultural University); Ganyu Wang (Western University); Han Li (Huazhong Agricultural University); Yulong Wang (Huazhong Agricultural University); Hong Chen (Huazhong Agricultural University); Bin Gu (School of Artificial Intelligence, Jilin University, Mohamed bin Zayed University of Artificial Intelligence) </td></tr><tr><td><strong>Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination<strong></strong></strong></td></tr><tr><td>Ming Hu (Singapore Management University); Zhihao Yue (East China Normal University); Xiaofei Xie (Singapore Management University); Cheng Chen (East China Normal University); Yihao Huang (Nanyang Technological University); Xian Wei (Chinese Academy of Sciences); Xiang Lian (Kent State University); Yang Liu (Nanyang Technological University); Mingsong Chen (East China Normal University) </td></tr><tr><td><strong>VertiMRF: Differentially Private Vertical Federated Data Synthesis<strong></strong></strong></td></tr><tr><td>Fangyuan Zhao (Xi’an Jiaotong University); Zitao Li (Alibaba Group); Xuebin Ren (Xi’an Jiaotong University); Bolin Ding (Alibaba Group); Shusen Yang (Xi’an Jiaotong University); Yaliang Li (Alibaba Group) </td></tr><tr><td><strong>On the Convergence of Zeroth-Order Federated Tuning for Large Language Models<strong></strong></strong></td></tr><tr><td>Zhenqing Ling (Sun Yat-sen University); Daoyuan Chen (Alibaba Group); Liuyi Yao (Alibaba Group); Yaliang Li (Alibaba Group); Ying Shen (Sun Yat-sen University, Pazhou Lab) </td></tr><tr><td><strong>FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model<strong></strong></strong></td></tr><tr><td>Feijie Wu (Purdue University); Zitao Li (Alibaba Group); Yaliang Li (Alibaba Group); Bolin Ding (Alibaba Group); Jing Gao (Purdue University) </td></tr><tr><td><strong>Federated Graph Learning with Structure Proxy Alignment<strong></strong></strong></td></tr><tr><td>Xingbo Fu (University of Virginia); Zihan Chen (University of Virginia); Binchi Zhang (University of Virginia); Chen Chen (University of Virginia); Jundong Li (University of Virginia) </td></tr><tr><td><strong>HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning<strong></strong></strong></td></tr><tr><td>Zhuoning Guo (Artificial Intelligence Thrust, Hong Kong University of Science and Technology (Guangzhou), Department of Computer Science and Engineering, Hong Kong University of Science and Technology); Duanyi Yao (Academy of Interdisciplinary Studies, Hong Kong University of Science and Technology); Qiang Yang (Department of Computer Science and Engineering, Hong Kong University of Science and Technology); Hao Liu (Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou), Department of Computer Science and Engineering, The Hong Kong University of Science and Technology) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 11:00-13:00, Room 134<br>Theme: Health & Molecular Data </strong><br><strong>Session Chair: Carl Yang (Emory University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction<strong></strong></strong></td></tr><tr><td>Muhao Xu (Institute of Information Science, Beijing Jiaotong University, Beijing Key Laboratory of Advanced Information Science and Network Technology); Zhenfeng Zhu (Institute of Information Science, Beijing Jiaotong University, Beijing Key Laboratory of Advanced Information Science and Network Technology); Youru Li (Institute of Information Science, Beijing Jiaotong University, Beijing Key Laboratory of Advanced Information Science and Network Technology); Shuai Zheng (Institute of Information Science, Beijing Jiaotong University, Beijing Key Laboratory of Advanced Information Science and Network Technology); Yawei Zhao (Medical Big Data Research Center, Chinese PLA General Hospital); Kunlun He (Medical Big Data Research Center, Chinese PLA General Hospital); Yao Zhao (Institute of Information Science, Beijing Jiaotong University, Beijing Key Laboratory of Advanced Information Science and Network Technology) </td></tr><tr><td><strong>Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models<strong></strong></strong></td></tr><tr><td>Yuan Zhong (The Pennsylvania State University); Xiaochen Wang (The Pennsylvania State University); Jiaqi Wang (The Pennsylvania State University); Xiaokun Zhang (Dalian University of Technology); Yaqing Wang (Purdue University); Mengdi Huai (Iowa State University); Cao Xiao (GE Healthcare); Fenglong Ma (The Penn State University) </td></tr><tr><td><strong>ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup<strong></strong></strong></td></tr><tr><td>Yongxin Xu (Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University); Xinke Jiang (Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University); Xu Chu (Key Laboratory of High Confidence Software Technologies, Ministry of Education, SCS & CFCS, Peking University); Yuzhen Xiao (Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University); Chaohe Zhang (Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University); Hongxin Ding (Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University); Junfeng Zhao (Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University); Yasha Wang (Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University); Bing Xie (Key Laboratory of High Confidence Software Technologies, Ministry of Education, Peking University) </td></tr><tr><td><strong>Brant-X: A Unified Physiological Signal Alignment Framework<strong></strong></strong></td></tr><tr><td>Daoze Zhang (Zhejiang University); Zhizhang Yuan (Zhejiang University); Junru Chen (Zhejiang University); Kerui Chen (Zhejiang University); Yang Yang (Zhejiang University) </td></tr><tr><td><strong>Mutual Distillation Extracting Spatial-temporal Knowledge for Lightweight Multi-channel Sleep Stage Classification<strong></strong></strong></td></tr><tr><td>Ziyu Jia (Institute of Automation, Chinese Academy of Sciences); Haichao Wang (Tsinghua-Berkeley Shenzhen Institute, Tsinghua University); Yucheng Liu (University of Southern California); Tianzi Jiang (Institute of Automation, Chinese Academy of Sciences) </td></tr><tr><td><strong>Advancing Molecule Invariant Representation via Privileged Substructure Identification<strong></strong></strong></td></tr><tr><td>Ruijia Wang (Beijing University of Posts and Telecommunications, China Telecom Cloud Computing Research Institute); Haoran Dai (Beijing University of Posts and Telecommunications); Cheng Yang (Beijing University of Posts and Telecommunications); Le Song (BioMap Research, MBZUAI); Chuan Shi (Beijing University of Post and Telecommunication) </td></tr><tr><td><strong>Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge<strong></strong></strong></td></tr><tr><td>Yizhen Luo (Institute for AI Industry Research (AIR), Tsinghua University); Kai Yang (Institute for AI Industry Research (AIR), Tsinghua University); Massimo Hong (Institute for AI Industry Research (AIR), Tsinghua University); Xing Yi Liu (Institute for AI Industry Research (AIR), Tsinghua University); Zikun Nie (Institute for AI Industry Research (AIR), Tsinghua University); Hao Zhou (Institute for AI Industry Research (AIR), Tsinghua University); Zaiqing Nie (Institute for AI Industry Research (AIR), Tsinghua University, Pharmolix Inc.) </td></tr><tr><td><strong>Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models<strong></strong></strong></td></tr><tr><td>Xu Shen (Jilin University); Yili Wang (Jilin University); Kaixiong Zhou (Massachusetts Institute of Technology); Shirui Pan (Griffith University); Xin Wang (Jilin University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 16:30-18:30, Room 116<br>Theme: Ranking and Retrieval</strong><br><strong>Session Chair: Tong Chen (The University of Queensland)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Ranking with Slot Constraints</strong></td></tr><tr><td>Wentao Guo (Department of Computer Science, Cornell University); Andrew Wang (Department of Computer Science, Cornell University); Bradon Thymes (Department of Computer Science, Cornell University); Thorsten Joachims (Department of Computer Science, Cornell University)</td></tr><tr><td><strong>Meta Clustering of Neural Bandits</strong></td></tr><tr><td>Yikun Ban (University of Illinois at Urbana-Champaign); Yunzhe Qi (University of Illinois at Urbana-Champaign); Tianxin Wei (University of Illinois, Urbana-Champaign); Lihui Liu(Univesity of Illinois, Urbana-Champaign); Jingrui He (University of Illinois, Urbana Champaign)</td></tr><tr><td><strong>Neural Retrievers are Biased Towards LLM-Generated Content</strong></td></tr><tr><td>Sunhao Dai (Gaoling School of Artificial Intelligence, Renmin University of China); Yuqi Zhou (Gaoling School of Artificial Intelligence, Renmin University of China); Liang Pang (CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences); Weihao Liu (Gaoling School of Artificial Intelligence, Renmin University of China); Xiaolin Hu (Gaoling School of Artificial Intelligence, Renmin University of China); Yong Liu (Gaoling School of Artificial Intelligence, Renmin University of China); Xiao Zhang (Gaoling School of Artificial Intelligence, Renmin University of China); Gang Wang (Noah’s Ark Lab, Huawei); Jun Xu (Gaoling School of Artificial Intelligence, Renmin University of China)</td></tr><tr><td><strong>Reliable Confidence Intervals for Information Retrieval Evaluation using Generative A.I.</strong></td></tr><tr><td>Harrie Oosterhuis (Google Research, Radboud University); Rolf Jagerman (Google Research); Zhen Qin (Google Research); Xuanhui Wang (Google Research); Michael Bendersky (Google Research)</td></tr><tr><td><strong>Extreme Meta-classification for Large-Scale Zero-Shot Retrieval</strong></td></tr><tr><td>Sachin Yadav (Microsoft Research); Deepak Saini (Microsoft); Anirudh Buvanesh (Microsoft Research); Bhawna Paliwal (Microsoft Research); Kunal Dahiya (Indian Institute of Technology Delhi); Siddarth Asokan (Microsoft Research); Yashoteja Prabhu (Microsoft Research); Jian Jiao (Microsoft); Manik Varma (Microsoft Research)</td></tr><tr><td><strong>Towards Robust Information Extraction via Binomial Distribution Guided Counterpart Sequence</strong></td></tr><tr><td>Yinhao Bai (College of Software, Nankai University); Yuhua Zhao (College of Software, Nankai University); Zhixin Han (College of Software, Nankai University); Hang Gao (College of Artificial Intelligence, Tianjin University of Science and Technology); Chao Xue (JD Explore Academy); Mengting Hu (College of Software, Nankai University)</td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 16:30-18:30, Room 120<br>Theme: Graph Mining & Algorithms</strong><br><strong>Session Chair: Karina Gibert (Universitat Polit猫cnica de Catalunya)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Revisiting Local PageRank Estimation on Undirected Graphs: Simple and Optimal<strong></strong></strong></td></tr><tr><td>Hanzhi Wang (Renmin University of China) </td></tr><tr><td><strong>Reimagining Graph Classification from a Prototype View with Optimal Transport: Algorithm and Theorem<strong></strong></strong></td></tr><tr><td>Chen Qian (Gaoling School of Artificial Intelligence, Renmin University of China); Huayi Tang(Gaoling School of Artificial Intelligence, Renmin University of China); Hong Liang (School of Electronic and Computer Engineering, Peking University); Yong Liu (Gaoling School of Artificial Intelligence, Renmin University of China) </td></tr><tr><td><strong>A Unified Core Structure in Multiplex Networks: From Finding the Densest Subgraph to Modeling User Engagement<strong></strong></strong></td></tr><tr><td>Farnoosh Hashemi (Cornell University); Ali Behrouz (Cornell University) </td></tr><tr><td><strong>Influence Maximization via Graph Neural Bandits<strong></strong></strong></td></tr><tr><td>Yuting Feng (Department of Electrical & Computer Engineering, National University of Singapore); Vincent Tan (Department of Mathematics, National University of Singapore); Bogdan Cautis (CNRS LISN, Universit茅 Paris Saclay) </td></tr><tr><td><strong>Predicting Cascading Failures with a Hyperparametric Diffusion Model<strong></strong></strong></td></tr><tr><td>Bin Xiang (CNRS@CREATE); Bogdan Cautis (University of Paris-Saclay, CNRS LISN); Xiaokui Xiao (National University of Singapore); Olga Mula (Eindhoven University of Technology); Dusit Niyato (Nanyang Technological University); Laks V.S. Lakshmanan (University of British Columbia) </td></tr><tr><td><strong>Unsupervised Alignment of Hypergraphs with Different Scales<strong></strong></strong></td></tr><tr><td>Manh Tuan Do (KAIST); Kijung Shin (KAIST) </td></tr><tr><td><strong>Hierarchical Neural Constructive Solver for Real-world TSP Scenarios<strong></strong></strong></td></tr><tr><td>Yong Liang Goh (School of Computing, National University of Singapore); Zhiguang Cao (Singapore Management University); Yining Ma (National University of Singapore); Yanfei Dong (School of Computing, National University of Singapore); Mohammed Haroon Dupty (School of Computing, National University of Singapore); Wee Sun Lee (School of Computing, National University of Singapore) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 16:30-18:30, Room 121<br>Theme: Security & Privacy</strong><br><strong>Session Chair: Simon S. Woo (Sungkyunkwan University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>RCTD: Reputation-Constrained Truth Discovery in Sybil Attack Crowdsourcing Environment<strong></strong></strong></td></tr><tr><td>Xing Jin (School of Cyberspace, Hangzhou Dianzi University); Zhihai Gong (School of Cyberspace, Hangzhou Dianzi University); Jiuchuan Jiang (School of Information Engineering, Nanjing University of Finance and Economics); Chao Wang (Research Center for Data Hub and Security, Zhejiang Lab); Jian Zhang (School of Cyberspace, Hangzhou Dianzi University); Zhen Wang (School of Cyberspace, Hangzhou Dianzi University, Experimental Center of Data Science and Intelligent Decision-Making, Hangzhou Dianzi University) </td></tr><tr><td><strong>CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent<strong></strong></strong></td></tr><tr><td>Liang-bo Ning (The Hong Kong Polytechnic University); Shijie Wang (The Hong Kong Polytechnic University); Wenqi Fan (Department of Computing & Department of Management and Marketing, The Hong Kong Polytechnic University); Qing Li (The Hong Kong Polytechnic University); Xin Xu (The Hong Kong Polytechnic University); Hao Chen (The Hong Kong Polytechnic University); Feiran Huang (Jinan University) </td></tr><tr><td><strong>BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning<strong></strong></strong></td></tr><tr><td>Yi Liu (City University of Hong Kong); Cong Wang (City University of Hong Kong); Xingliang Yuan (The University of Melbourne) </td></tr><tr><td><strong>Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness<strong></strong></strong></td></tr><tr><td>Dingrong Wang (Golisano College of Computing and Information Sciences, Rochester Institute of Technology); Hitesh Sapkota (Amazon Inc.); Zhiqiang Tao (Golisano College of Computing and Information Sciences, Rochester Institute of Technology); Qi Yu (Golisano College of Computing and Information Sciences, Rochester Institute of Technology) </td></tr><tr><td><strong>Fake News in Sheep’s Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks<strong></strong></strong></td></tr><tr><td>Jiaying Wu (National University of Singapore); Jiafeng Guo (University of Chinese Academy of Sciences, Institute of Computing Technology, CAS); Bryan Hooi (National University of Singapore) </td></tr><tr><td><strong>Privacy-Preserved Neural Graph Databases<strong></strong></strong></td></tr><tr><td>Qi Hu(Department of CSE, Hong Kong University of Science and Technology); Haoran Li(Department of CSE, Hong Kong University of Science and Technology); Jiaxin Bai (Department of CSE, Hong Kong University of Science and Technology); Zihao Wang (Department of CSE, Hong Kong University of Science and Technology); Yangqiu Song (Department of CSE, Hong Kong University of Science and Technology) </td></tr><tr><td><strong>DPSW-Sketch: A Differentially Private Sketch Framework for Frequency Estimation over Sliding Windows<strong></strong></strong></td></tr><tr><td>Yiping Wang (East China Normal University); Yanhao Wang(East China Normal University); Cen Chen (East China Normal University, The State Key Laboratory of Blockchain and Data Security, Zhejiang University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 16:30-18:30, Room 122<br>Theme: Temporal Graphs II</strong><br><strong>Session Chair: Petko Bogdanov (University at Albany-SUNY)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Efficient and Effective Implicit Dynamic Graph Neural Network<strong></strong></strong></td></tr><tr><td>Yongjian Zhong (University of Iowa), Hieu Vu (University of Iowa), Tianbao Yang (Texas A&M University – College Station), Bijaya Adhikari (University of Iowa) </td></tr><tr><td><strong>Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling<strong></strong></strong></td></tr><tr><td>Siwei Zhang (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Xi Chen (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Yun Xiong (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Xixi Wu (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Yao Zhang (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Yongrui Fu (Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University); Yinglong Zhao (Ant Group); Jiawei Zhang (IFM Lab, Department of Computer Science, University of California, Davis) </td></tr><tr><td><strong>Repeat-Aware Neighbor Sampling for Dynamic Graph Learning<strong></strong></strong></td></tr><tr><td>Tao Zou (Beihang University); Yuhao Mao (Beihang University); Junchen Ye (Beihang University); Bowen Du (Beihang University) </td></tr><tr><td><strong>Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs<strong></strong></strong></td></tr><tr><td>Hao Li (Electronic Information School, Wuhan University); Hao Jiang (Electronic Information School, Wuhan University); Fan Jiajun (Electronic Information School, Wuhan University); Dongsheng Ye (Electronic Information School, Wuhan University); Liang Du (Electronic Information School, Wuhan University) </td></tr><tr><td><strong>Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck<strong></strong></strong></td></tr><tr><td>Sangwoo Seo (KAIST); Sungwon Kim (KAIST); Jihyeong Jung (KAIST); Yoonho Lee (KAIST); Chanyoung Park (KAIST) </td></tr><tr><td><strong>LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?<strong></strong></strong></td></tr><tr><td>Zeyang Zhang (DCST, Tsinghua University); Xin Wang (DCST, BNRist, Tsinghua University); Ziwei Zhang (DCST, Tsinghua University); Haoyang Li (DCST, Tsinghua University); Yijian Qin (DCST, Tsinghua University); Wenwu Zhu (DCST, BNRist, Tsinghua University) </td></tr><tr><td><strong>Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach<strong></strong></strong></td></tr><tr><td>Yicong Li (The Education University of Hong Kong, University of Technology Sydney); Yu Yang (The Hong Kong Polytechnic University); Jiannong Cao (The Hong Kong Polytechnic University); Shuaiqi Liu (The Hong Kong Polytechnic University); Haoran Tang (The Hong Kong Polytechnic University); Guandong Xu (The Education University of Hong Kong, University of Technology Sydney) </td></tr><tr><td><strong>TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics<strong></strong></strong></td></tr><tr><td>Chang Liu (Department of Electronic Engineering, BNRist, Tsinghua University); Jingtao Ding (Department of Electronic Engineering, BNRist, Tsinghua University); Yiwen Song (Shenzhen International Graduate School, Tsinghua University); Yong Li (Department of Electronic Engineering, BNRist, Tsinghua University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 16:30-18:30, Room 129-130<br>Theme: Self- & Semi-supervised Learning</strong><br><strong>Session Chair: Dunja Mladenic (Jozef Stefan Institute)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Geometric View of Soft Decorrelation in Self-Supervised Learning<strong></strong></strong></td></tr><tr><td>Yifei Zhang (The Chinese University of Hong Kong); Hao Zhu (CSIRO); Zixing Song (The Chinese University of Hong Kong); Yankai Chen (Department of Computer Science and Engineering, The Chinese University of Hong Kong); Xinyu Fu (Department of Computer Science and Engineering, The Chinese University of Hong Kong); Ziqiao Meng (The Chinese University of Hong Kong); Piotr Koniusz (Data61, CSIRO); Irwin King (The Chinese University of Hong Kong) </td></tr><tr><td><strong>Self-Supervised Learning for Graph Dataset Condensation<strong></strong></strong></td></tr><tr><td>Yuxiang Wang (School of Computer Science, Wuhan University); Xiao Yan (Centre for Perceptual and Interactive Intelligence (CPII)); Shiyu Jin (School of Computer Science, Wuhan University); Hao Huang (School of Computer Science, Wuhan University); Quanqing Xu (OceanBase, Ant Group); Qingchen Zhang (School of Computer Science and Technology, Hainan University); Bo Du (School of Computer Science, Wuhan University); Jiawei Jiang (School of Computer Science, Wuhan University) </td></tr><tr><td><strong>Representation Learning of Geometric Trees<strong></strong></strong></td></tr><tr><td>Zheng Zhang (Emory University); Allen Zhang (Georgia Institute of Technology); Ruth Nelson (Yale University); Giorgio Ascoli (George Mason University); Liang Zhao (Emory University) </td></tr><tr><td><strong>Reserving-Masking-Reconstruction Model for Self-Supervised Heterogeneous Graph Representation<strong></strong></strong></td></tr><tr><td>Haoran Duan (Yunnan University); Cheng Xie (Yunnan University); Linyu Li (Yunnan University) </td></tr><tr><td><strong>Self-Supervised Denoising through Independent Cascade Graph Augmentation for Robust Social Recommendation<strong></strong></strong></td></tr><tr><td>Youchen Sun (Nanyang Technological University); Zhu Sun (A*STAR Centre for Frontier AI Research, Singapore University of Technology and Design); Yingpeng Du (Nanyang Technological University); Jie Zhang (Nanyang Technological University); Yew Soon Ong (A*STAR Centre for Frontier AI Research, Nanyang Technological University) </td></tr><tr><td><strong>Divide and Denoise: Empowering Simple Models for Robust Semi-Supervised Node Classification against Label Noise<strong></strong></strong></td></tr><tr><td>Kaize Ding (Northwestern University); Xiaoxiao Ma (Macquarie University); Yixin Liu (Monash University); Shirui Pan (Griffith University) </td></tr><tr><td><strong>Resurrecting Label Propagation for Graphs with Heterophily and Label Noise<strong></strong></strong></td></tr><tr><td>Yao Cheng (East China Normal University); Caihua Shan (Microsoft Research Asia); Yifei Shen (Microsoft Research Asia); Xiang Li (East China Normal University); Siqiang Luo (Nanyang Technological University); Dongsheng Li (Microsoft Research Asia) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 16:30-18:30, Room 131-132<br>Theme: Time Series II</strong><br><strong>Session Chair: Abdullah Mueen (University of New Mexico)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Dataset Condensation for Time Series Classification via Dual Domain Matching<strong></strong></strong></td></tr><tr><td>Zhanyu Liu (Shanghai Jiao Tong University); Ke Hao (Shanghai Jiao Tong University); Guanjie Zheng (Shanghai Jiao Tong University); Yanwei Yu (Ocean University of China) </td></tr><tr><td><strong>CAFO: Feature-Centric Explanation on Time Series Classification<strong></strong></strong></td></tr><tr><td>Jaeho Kim (Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST)); Seok-Ju Hahn (Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST)); Yoontae Hwang (Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST)); Junghye Lee (Technology Management, Economics and Policy Program, Seoul National University (SNU), Graduate School of Engineering Practice & Institute of Engineering Research, Seoul National University (SNU)); Seulki Lee (Computer Science and Engineering & Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST)) </td></tr><tr><td><strong>Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution Classification<strong></strong></strong></td></tr><tr><td>Ruize Shi (Huazhong University of Science and Technology); Hong Huang (Huazhong University of Science and Technology); Kehan Yin (Huazhong University of Science and Technology); Wei Zhou (Huazhong University of Science and Technology); Hai Jin (Huazhong University of Science and Technology) </td></tr><tr><td><strong>ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification<strong></strong></strong></td></tr><tr><td>Xuan-May Le (School of Computing and Information Systems, University of Melbourne); Ling Luo (School of Computing and Information Systems, University of Melbourne); Uwe Aickelin (School of Computing and Information Systems, University of Melbourne); Minh-Tuan Tran (Department of Data Science and AI, Monash University) </td></tr><tr><td><strong>Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series<strong></strong></strong></td></tr><tr><td>Kohei Obata (SANKEN, Osaka University); Koki Kawabata (SANKEN, Osaka University); Yasuko Matsubara (SANKEN, Osaka University); Yasushi Sakurai (SANKEN, Osaka University) </td></tr><tr><td><strong>ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions<strong></strong></strong></td></tr><tr><td>Zhichen Lai (Department of Computer Science, Aalborg University); Dalin Zhang (Department of Computer Science, Aalborg University); Huan Li (The State Key Laboratory of Blockchain and Data Security, Zhejiang University); Dongxiang Zhang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University); Hua Lu (Department of People and Technology, Roskilde University); Christian S. Jensen (Department of Computer Science, Aalborg University) </td></tr><tr><td><strong>Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask<strong></strong></strong></td></tr><tr><td>Zineb Senane (Motherbrain, EQT Group, KTH Royal Institute of Technology); Lele Cao (Motherbrain, EQT Group); Valentin Leonhard Buchner (Motherbrain, EQT Group); Yusuke Tashiro (Mitsubishi UFJ Trust Investment Technology Institute); Lei You (Technical University of Denmark); Pawel Andrzej Herman (KTH Royal Institute of Technology); Mats Nordahl (KTH Royal Institute of Technology); Ruibo Tu (KTH Royal Institute of Technology); Vilhelm von Ehrenheim (Motherbrain, EQT Group, QA.tech) </td></tr><tr><td><strong>CONFIDE: Contextual Finite Difference Modelling of PDEs<strong></strong></strong></td></tr><tr><td>Ori Linial (Technion – Israel Institute of Technology); Orly Avner (Bosch Center for Artificial Inteligence); Dotan Di Castro (Bosch Center for Artificial Inteligence) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 16:30-18:30, Room 133<br>Theme: Adaptive Learning </strong><br><strong>Session Chair: Sai Ravela (MIT)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuning<strong></strong></strong></td></tr><tr><td>Wangyang Ying (Arizona State University); Dongjie Wang (The University of Kansas); Xuanming Hu (Arizona State University); Yuanchun Zhou (Computer Network Information Center, Chinese Academy of Sciences); Charu C. Aggarwal (International Business Machines T. J. Watson Research Center); Yanjie Fu (Arizona State University) </td></tr><tr><td><strong>Sketch-Based Replay Projection for Continual Learning<strong></strong></strong></td></tr><tr><td>Jack Julian (School of Computer Science, University of Auckland); Yun Sing Koh (School of Computer Science, University of Auckland); Albert Bifet (AI Institute, The University of Waikato, LTCI, T茅l茅com Paris, IP Paris) </td></tr><tr><td><strong>Topology-aware Embedding Memory for Continual Learning on Expanding Networks<strong></strong></strong></td></tr><tr><td>Xikun Zhang (University of Sydney); Dongjin Song (University of Connecticut); Yixin Chen (Washington University, Saint Louis); Dacheng Tao (University of Sydney) </td></tr><tr><td><strong>Personalized Federated Continual Learning via Multi-Granularity Prompt<strong></strong></strong></td></tr><tr><td>Hao Yu (School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics); Xin Yang (School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics); Xin Gao (School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics); Yan Kang (Webank); Hao Wang (College of Computer Science, Sichuan University); Junbo Zhang (JD Intelligent Cities Research, JD iCity, JD Technology); Tianrui Li (School of Computing and Artificial Intelligence, Southwest Jiaotong University) </td></tr><tr><td><strong>Improved Active Covering via Density-Based Space Transformation<strong></strong></strong></td></tr><tr><td>MohammadHossein Bateni (Google Research); Hossein Esfandiari (Google Research); Samira HosseinGhorban (School of Computer Science, Institute for Research in Fundamental Sciences); Alipasha Montaseri (Sharif University of Technology) </td></tr><tr><td><strong>Handling Varied Objectives by Online Decision Making<strong></strong></strong></td></tr><tr><td>Lanjihong Ma (National Key Laboratory for Novel Software Technology, School of Artificial Intelligence, Nanjing University); Zhen-Yu Zhang (Center for Advanced Intelligence Project, RIKEN); Yao-Xiang Ding (State Key Lab of CAD & CG, Zhejiang University); Zhi-Hua Zhou (National Key Laboratory for Novel Software Technology, School of Artificial Intelligence, Nanjing University) </td></tr><tr><td><strong>Online Drift Detection with Maximum Concept Discrepancy<strong></strong></strong></td></tr><tr><td>Ke Wan (University of Illinois at Urbana-Champaign); Yi Liang (Fudan University); Susik Yoon (Korea University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Wednesday, August 28 16:30-18:30, Room 134<br>Theme: Deep Learning II </strong><br><strong>Session Chair: Mahsa Salehi (Monash University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Learning from Emergence: A Study on Proactively Inhibiting the Monosemantic Neurons of Artificial Neural Networks<strong></strong></strong></td></tr><tr><td>Jiachuan Wang (The Hong Kong University of Science and Technology); Shimin Di (The Hong Kong University of Science and Technology); Lei Chen (The Hong Kong University of Science and Technology (Guangzhou), The Hong Kong University of Science and Technology); Charles Wang Wai Ng (The Hong Kong University of Science and Technology (Guangzhou), The Hong Kong University of Science and Technology) </td></tr><tr><td><strong>Dual-Assessment Driven Pruning: Iterative Optimizing Layer-wise Sparsity for Large Language Model<strong></strong></strong></td></tr><tr><td>Qinghui Sun (Alibaba Group); Weilun Wang (Alibaba Group); Yanni Zhu (Alibaba Group); Shenghuan He (Alibaba Group); Hao Yi (Alibaba Group); Zehua Cai (Alibaba Group); Hong Liu (Alibaba Group) </td></tr><tr><td><strong>Hypformer: Exploring Efficient Transformer Fully in Hyperbolic Space<strong></strong></strong></td></tr><tr><td>Menglin Yang (Yale University); Harshit Verma (Birla Institute of Technology and Science); Ce Zhang (Yale University); Jiahong Liu (The Chinese University of Hong Kong); Irwin King (The Chinese University of Hong Kong); Rex Ying (Yale University) </td></tr><tr><td><strong>Knowledge Distillation with Perturbed Loss: From a Vanilla Teacher to a Proxy Teacher<strong></strong></strong></td></tr><tr><td>Rongzhi Zhang (Georgia Institute of Technology); Jiaming Shen (Google Deepmind); Tianqi Liu (Google Deepmind); Jialu Liu (Google); Michael Bendersky (Google Deepmind); Marc Najork (Google); Chao Zhang (Georgia Institute of Technology) </td></tr><tr><td><strong>SiGeo: Sub-One-Shot NAS via Geometry of Loss Landscape<strong></strong></strong></td></tr><tr><td>Hua Zheng (Northeastern University); Kuang-Hung Liu (Meta); Igor Fedorov (Meta); Xin Zhang (Meta); Wen-Yen Chen (Meta); Wei Wen (Meta) </td></tr><tr><td><strong>Rotative Factorization Machines<strong></strong></strong></td></tr><tr><td>Zhen Tian (Gaoling School of Artificial Intelligence, Renmin University of China, Beijing Key Laboratory of Big Data Management and Analysis Methods); Yuhong Shi (Zhejiang University); Xiangkun Wu (Zhejiang University); Xin Zhao (Gaoling School of Artificial Intelligence, Renmin University of China, Beijing Key Laboratory of Big Data Management and Analysis Methods); Ji-Rong Wen (Gaoling School of Artificial Intelligence, Renmin University of China, Beijing Key Laboratory of Big Data Management and Analysis Methods) </td></tr><tr><td><strong>URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering<strong></strong></strong></td></tr><tr><td>Ge Teng (Zhejiang University); Ting Mao (Alibaba Cloud); Chen Shen (Alibaba Cloud); Xiang Tian (Zhejiang University, Zhejiang University Embedded System Engineering Research Center, Ministry of Education of China); Xuesong Liu (Zhejiang University, Zhejiang University Embedded System Engineering Research Center, Ministry of Education of China); Yaowu Chen (Zhejiang University, Zhejiang University Embedded System Engineering Research Center, Ministry of Education of China); Jieping Ye (Alibaba Cloud) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 11:00-13:00, Room 116<br>Theme: Sequential Recommendations</strong><br><strong>Session Chair: Shoujin Wang (University of Technology Sydney)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Explicit and Implicit Modeling via Dual-Path Transformer for Behavior Set-informed Sequential Recommendation</strong></td></tr><tr><td>Ming Chen (College of Computer Science and Software Engineering, Shenzhen University); Weike Pan (College of Computer Science and Software Engineering, Shenzhen University); Zhong Ming (Shenzhen University, Shenzhen Technology University)</td></tr><tr><td><strong>Probabilistic Attention for Sequential Recommendation</strong></td></tr><tr><td>Yuli Liu (Qinghai University, Qinghai Provincial Key Laboratory of Media Integration Technology and Communication); Christian Walder (Google Research, Brain Team); Lexing Xie (Australian National University, Data61 CSIRO); Yiqun Liu (Department of Computer Science and Technology, Tsinghua University, Zhongguancun Laboratory)</td></tr><tr><td><strong>Dataset Regeneration for Sequential Recommendation</strong></td></tr><tr><td>Mingjia Yin (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Hao Wang (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Wei Guo (Huawei Singapore Research Center); Yong Liu (Huawei Singapore Research Center); Suojuan Zhang (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Sirui Zhao (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Defu Lian (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence); Enhong Chen (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence)</td></tr><tr><td><strong>Disentangled Multi-interest Representation Learning for Sequential Recommendation</strong></td></tr><tr><td>Yingpeng Du (Nanyang Technological University); Ziyan Wang (Nanyang Technological University); Zhu Sun (Singapore University of Technology and Design); Yining Ma (Nanyang Technological University); Hongzhi Liu (Peking University); Jie Zhang (Nanyang Technological University)</td></tr><tr><td><strong>Pre-Training with Transferable Attention for Addressing Market Shifts in Cross-Market Sequential Recommendation</strong></td></tr><tr><td>Chen Wang (University of Illinois Chicago); Ziwei Fan (Amazon); Liangwei Yang (Salesforce AI Research); Mingdai Yang (The University of Chicago); Xiaolong Liu (University of Illinois Chicago); Zhiwei Liu (Salesforce AI Research); Philip Yu (Tsinghua University)</td></tr><tr><td><strong>ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI Recommendation</strong></td></tr><tr><td>Shanshan Feng (Centre for Frontier AI Research, A<em>STAR, Institute of High Performance Computing, A</em>STAR); Feiyu Meng (University of Electronic Science and Technology of China); Lisi Chen (University of Electronic Science and Technology of China); Shuo Shang (University of Electronic Science and Technology of China); Yew Soon Ong (Centre for Frontier AI Research, A*STAR, Nanyang Technological University)</td></tr><tr><td><strong>Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations</strong></td></tr><tr><td>Jing Long (The University of Queensland); Guanhua Ye (Beijing University of Posts and Telecommunications); Tong Chen (The University of Queensland); Yang Wang (Hefei University of Technology); Meng Wang (Hefei University of Technology); Hongzhi Yin (The University of Queensland)</td></tr><tr><td><strong>Going Where, by Whom, and at What Time: Next Location Prediction Considering User Preference and Temporal Regularity</strong></td></tr><tr><td>Tianao Sun (School of Software, Shandong University); Ke Fu (School of Software, Shandong University); Weiming Huang (School of Computer Science and Engineering, Nanyang Technological University); Kai Zhao (Robinson College of Business, Georgia State University); Yongshun Gong (School of Software, Shandong University); Meng Chen (School of Software, Shandong University)</td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 11:00-13:00, Room 120<br>Theme: Graph ML</strong><br><strong>Session Chair: Samira Maghool (University of Milan)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Enhancing Contrastive Learning on Graphs with Node Similarity<strong></strong></strong></td></tr><tr><td>Hongliang Chi (Rensselaer Polytechnic Institute); Yao Ma (Rensselaer Polytechnic Institute) </td></tr><tr><td><strong>Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute<strong></strong></strong></td></tr><tr><td>Shinji Tajima (Nagoya Institute of Technology); Ren Sugihara (Nagoya Institute of Technology); Ryota Kitahara (Nagoya Institute of Technology); Masayuki Karasuyama (Nagoya Institute of Technology) </td></tr><tr><td><strong>A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs<strong></strong></strong></td></tr><tr><td>Amitoz Azad (Singapore Management University); Yuan Fang (Singapore Management University) </td></tr><tr><td><strong>Flexible Graph Neural Diffusion with Latent Class Representation Learning<strong></strong></strong></td></tr><tr><td>Liangtian Wan (Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology); Huijin Han (Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology); Lu Sun (Department of Communication Engineering, Institute of Information Science Technology, Dalian Martime University); Zixun Zhang (School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen); Zhaolong Ning (School of Communication and Information Engineering, Chongqing University of Post and Telecommunications); Xiaoran Yan (Research Center for Data Hub and Security, Zhejiang Lab); Feng Xia (School of Computing Technologies, RMIT University) </td></tr><tr><td><strong>DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback<strong></strong></strong></td></tr><tr><td>Yiqing Wu (Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences); Ruobing Xie (Tencent); Zhao Zhang(Institute of Computing Technology, Chinese Academy of Sciences); Xu Zhang (Tencent); Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University, Zhongguancun Laboratory); Leyu Lin (Tencent); Zhanhui Kang (Tencent); Yongjun Xu (Institute of Computing Technology, Chinese Academy of Sciences) </td></tr><tr><td><strong>Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning<strong></strong></strong></td></tr><tr><td>Danqing Wang (Language Technologies Institute, Carnegie Mellon University); Antonis Antoniades (University of California, Santa Barbara); Kha-Dinh Luong (University of California, Santa Barbara); Edwin Zhang (Harvard University, Founding); Mert Kosan (University of California, Santa Barbara); Jiachen Li (University of California, Santa Barbara); Ambuj Singh (University of California, Santa Barbara); William Yang Wang (University of California, Santa Barbara); Lei Li (Language Technologies Institute, Carnegie Mellon University) </td></tr><tr><td><strong>PolygonGNN: Representation Learning for Polygonal Geometries with Heterogeneous Visibility Graph<strong></strong></strong></td></tr><tr><td>Dazhou Yu (Emory University); Yuntong Hu (Emory University); Yun Li (Emory University); Liang Zhao (Emory University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 11:00-13:00, Room 121<br>Theme: Optimization</strong><br><strong>Session Chair: Fabio Vandin (University of Padova)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>GEO: Generative Engine Optimization<strong></strong></strong></td></tr><tr><td>Pranjal Aggarwal (Indian Institute of Technology, Delhi); Vishvak Murahari (Princeton University); Tanmay Rajpurohit (Independent); Ashwin Vijayakumar (Independent); Karthik Narasimhan (Princeton University); Ameet Deshpande (Princeton University) </td></tr><tr><td><strong>Subspace Selection based Prompt Tuning with Nonconvex Nonsmooth Black-Box Optimization<strong></strong></strong></td></tr><tr><td>Haozhen Zhang (School of Artificial Intelligence, Jilin University); Hualin Zhang (Mohamed bin Zayed University of Artificial Intelligence); Bin Gu (School of Artificial Intelligence, Jilin University); Yi Chang (School of Artificial Intelligence, Jilin University, Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education) </td></tr><tr><td><strong>Efficient Decision Rule List Learning via Unified Sequence Submodular Optimization<strong></strong></strong></td></tr><tr><td>Linxiao Yang (DAMO Academy, Alibaba Group); Jingbang Yang (DAMO Academy, Alibaba Group); Liang Sun (DAMO Academy, Alibaba Group) </td></tr><tr><td><strong>FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML<strong></strong></strong></td></tr><tr><td>Brian Liu (Massachusetts Institute of Technology); Rahul Mazumder (Massachusetts Institute of Technology) </td></tr><tr><td><strong>BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization<strong></strong></strong></td></tr><tr><td>Zhongyi Pei (School of Software, BNRist, Tsinghua University); Zhiyao Cen (School of Software, BNRist, Tsinghua University); Yipeng Huang (School of Software, BNRist, Tsinghua University); Chen Wang (School of Software, EIRI, Tsinghua University); Lin Liu (School of Software, BNRist, Tsinghua University); Philip Yu (School of Software, Tsinghua University); Mingsheng Long (School of Software, BNRist, Tsinghua University); Jianmin Wang (School of Software, BNRist, Tsinghua University) </td></tr><tr><td><strong>Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion<strong></strong></strong></td></tr><tr><td>Hao Zeng (Cainiao Network); Jiaqi Wang (Tsinghua University); Avirup Das (University of Manchester); Junying He (Cainiao Network); Kunpeng Han (Cainiao Network); Haoyuan Hu (Cainiao Network); Mingfei Sun (University of Manchester) </td></tr><tr><td><strong>Max-Min Diversification with Asymmetric Distances<strong></strong></strong></td></tr><tr><td>Iiro Kumpulainen (University of Helsinki); Florian Adriaens (University of Helsinki, HIIT); Nikolaj Tatti (University of Helsinki, HIIT) </td></tr><tr><td><strong>BoKA: Bayesian Optimization based Knowledge Amalgamation for Multi-unknown-domain Text Classification<strong></strong></strong></td></tr><tr><td>Linzhu Yu (The State Key Laboratory of Blockchain and Data Security, Zhejiang University); Huan Li (The State Key Laboratory of Blockchain and Data Security, Zhejiang University); Ke Chen (The State Key Laboratory of Blockchain and Data Security, Zhejiang University); Lidan Shou (The State Key Laboratory of Blockchain and Data Security, Zhejiang University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 11:00-13:00, Room 122<br>Theme: Label Learning & Classification</strong><br><strong>Session Chair: Sara Abdali (Microsoft)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Label Learning Method Based on Tensor Projection<strong></strong></strong></td></tr><tr><td>Jing Li (School of Telecommunications Engineering, Xidian University); Quanxue Gao (School of Telecommunications Engineering, Xidian University); Qianqian Wang(School of Telecommunications Engineering, Xidian University); Cheng Deng (School of Electronic Engineering, Xidian University); Deyan Xie(School of Science and Information Science, Qingdao Agricultural University) </td></tr><tr><td><strong>Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features<strong></strong></strong></td></tr><tr><td>Siddhant Kharbanda (University of California, Los Angeles); Devaansh Gupta (Aalto University); Erik Schultheis (Aalto University); Atmadeep Banerjee (Aalto University); Cho-Jui Hsieh (University of California, Los Angeles); Rohit Babbar (Aalto University, University of Bath) </td></tr><tr><td><strong>Noisy Label Removal for Partial Multi-Label Learning<strong></strong></strong></td></tr><tr><td>Fuchao Yang (College of Software Engineering, Southeast University); Yuheng Jia (School of Computer Science and Engineering, Southeast University); Hui Liu (School of Computing & Information Sciences, Saint Francis University); Yongqiang Dong (School of Computer Science and Engineering, Southeast University); Junhui Hou (Department of Computer Science, City University of Hong Kong) </td></tr><tr><td><strong>FairMatch: Promoting Partial Label Learning by Unlabeled Samples<strong></strong></strong></td></tr><tr><td>Yuheng Jia (College of Software Engineering, Southeast University); Jiahao Jiang (School of Computer Science and Engineering, Southeast University); Hui Liu (School of Computing & Information Sciences, Saint Francis University); Junhui Hou (Department of Computer Science, City University of Hong Kong) </td></tr><tr><td><strong>Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning<strong></strong></strong></td></tr><tr><td>Hao-Zhe Liu (Nanjing University of Aeronautics and Astronautics); Ming-Kun Xie (Nanjing University of Aeronautics and Astronautics); Chen-Chen Zong (Nanjing University of Aeronautics and Astronautics); Sheng-Jun Huang (Nanjing University of Aeronautics and Astronautics) </td></tr><tr><td><strong>Iterative Weak Learnability and Multi-Class AdaBoost<strong></strong></strong></td></tr><tr><td>In-Koo Cho (Emory University, Hanyang University); Jonathan A. Libgober (University of Southern California); Cheng Ding (Emory University) </td></tr><tr><td><strong>AnyLoss: Transforming Classification Metrics into Loss Functions<strong></strong></strong></td></tr><tr><td>Doheon Han (University of Notre Dame); Nuno Moniz (University of Notre Dame); Nitesh Chawla (University of Notre Dame) </td></tr><tr><td><strong>A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation Reliability<strong></strong></strong></td></tr><tr><td>Sakhawat Hossain Saimon (Department of Computer Science, The University of Texas at San Antonio); Tanzira Najnin (Department of Computer Science, The University of Texas at San Antonio); Jianhua Ruan (Department of Computer Science, The University of Texas at San Antonio) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 11:00-13:00, Room 129-130<br>Theme: Knowledge & Reasoning</strong><br><strong>Session Chair: Carl Yang (Emory University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Conditional Logical Message Passing Transformer for Complex Query Answering<strong></strong></strong></td></tr><tr><td>Chongzhi Zhang (South China University of Technology); Zhiping Peng (Guangdong University of Petrochemical Technology, Jiangmen Polytechnic); Junhao Zheng (South China University of Technology); Qianli Ma (South China University of Technology) </td></tr><tr><td><strong>AsyncET: Asynchronous Representation Learning for Knowledge Graph Entity Typing<strong></strong></strong></td></tr><tr><td>Yun-Cheng Wang (University of Southern California); Xiou Ge (University of Southern California); Bin Wang (National University of Singapore); C.-C. Jay Kuo (University of Southern California) </td></tr><tr><td><strong>Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion<strong></strong></strong></td></tr><tr><td>Qinggang Zhang (The Hong Kong Polytechnic University); Keyu Duan (National University of Singapore); Junnan Dong (The Hong Kong Polytechnic University); Pai Zheng (Hong Kong Polytechnic University); Xiao Huang (The Hong Kong Polytechnic University) </td></tr><tr><td><strong>Path-based Explanation for Knowledge Graph Completion<strong></strong></strong></td></tr><tr><td>Heng Chang (Huawei Technologies Co., Ltd.); Jiangnan Ye (Huawei Technologies Co., Ltd.); Alejo Lopez-Avila (Huawei Technologies Co., Ltd.); Jinhua Du (Huawei Technologies Co., Ltd.); Jia Li (The Hong Kong University of Science and Technology) </td></tr><tr><td><strong>DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural Networks<strong></strong></strong></td></tr><tr><td>Zongsheng Cao (University of Chinese Academy of Sciences); Jing Li (School of Economics and Management, Tsinghua University); Zigan Wang (Shenzhen International Graduate School & School of Economics and Management, Tsinghua University); Jinliang Li (School of Economics and Management, Tsinghua University) </td></tr><tr><td><strong>How to Avoid Jumping to Conclusions: Measuring the Robustness of Outstanding Facts in Knowledge Graphs<strong></strong></strong></td></tr><tr><td>Hanhua Xiao (Singapore Management University); Yuchen Li (Singapore Management University); Yanhao Wang (East China Normal University); Panagiotis Karras (University of Copenhagen); Kyriakos Mouratidis (Singapore Management University); Natalia Rozalia Avlona (University of Copenhagen) </td></tr><tr><td><strong>Embedding Two-View Knowledge Graphs with Class Inheritance and Structural Similarity<strong></strong></strong></td></tr><tr><td>Kyuhwan Yeom (Computer Science, Yonsei University); Hyeongjun Yang (Computer Science, Yonsei University); Gayeon Park (Artificial Intelligence, Yonsei University); Myeongheon Jeon (Computer Science, Yonsei University); Yunjeong Ko (Computer Science, Yonsei University); Byungkook Oh (Computer Science and Engineering, Konkuk University); Kyong-Ho Lee (Computer Science, Yonsei University) </td></tr><tr><td><strong>SimDiff: Simple Denoising Probabilistic Latent Diffusion Model for Data Augmentation on Multi-modal Knowledge Graph<strong></strong></strong></td></tr><tr><td>Ran Li (Hong Kong University of Science and Technology); Shimin Di (Hong Kong University of Science and Technology); Lei Chen (Hong Kong University of Science and Technology (Guangzhou), Hong Kong University of Science and Technology); Xiaofang Zhou (Hong Kong University of Science and Technology) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 11:00-13:00, Room 131-132</strong><br><strong>Theme: Vision, Language, Audio</strong><br><strong>Session Chair: Eliana Pastor (Politecnico di Torino)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Neural Collapse Anchored Prompt Tuning for Generalizable Vision-Language Models<strong></strong></strong></td></tr><tr><td>Didi Zhu (Zhejiang University); Zexi Li (Zhejiang University); Min Zhang (College of Computer Science and Technology, Zhejiang University); Junkun Yuan (Zhejiang University); Jiashuo Liu (Tsinghua University); Kun Kuang (Zhejiang University); Chao Wu (Zhejiang University) </td></tr><tr><td><strong>Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model<strong></strong></strong></td></tr><tr><td>Jiang-Xin Shi (Nanjing University); Chi Zhang (Nanjing University); Tong Wei (Southeast University); Yu-Feng Li (Nanjing University) </td></tr><tr><td><strong>Routing Evidence for Unseen Actions in Video Moment Retrieval<strong></strong></strong></td></tr><tr><td>Guolong Wang (School of Information Technology & Management, University of International Business and Economics); Xun Wu (Tsinghua University); Zheng Qin (Tsinghua University); Liangliang Shi (School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Lab of AI, Shanghai Jiao Tong University) </td></tr><tr><td><strong>ReFound: Crafting a Foundation Model for Urban Region Understanding upon Language and Visual Foundations<strong></strong></strong></td></tr><tr><td>Congxi Xiao (School of Computer Science and Technology, University of Science and Technology of China); Jingbo Zhou (Business Intelligence Lab, Baidu Research); Yixiong Xiao (Business Intelligence Lab, Baidu Research); Jizhou Huang (Baidu Inc.); Hui Xiong (Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Department of Computer Science and Engineering, The Hong Kong University of Science and Technology) </td></tr><tr><td><strong>LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation<strong></strong></strong></td></tr><tr><td>Rebecca Salganik (University of Rochester); Xiaohao Liu (National University of Singapore); Yunshan Ma (National University of Singapore); Jian Kang (University of Rochester); Tat-seng Chua (National University of Singapore) </td></tr><tr><td><strong>Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain<strong></strong></strong></td></tr><tr><td>Amin Karimi Monsefi (Department of Computer Science and Engineering, The Ohio State University); Payam Karisani (Department of Computer Science, University of Illinois Urbana-Champaign); Mengxi Zhou (Department of Computer Science and Engineering, The Ohio State University); Stacey Choi (Department of Ophthalmology and Visual Science, The Ohio State University); Nathan Doble (Department of Ophthalmology and Visual Science, The Ohio State University); Heng Ji (Department of Computer Science, University of Illinois Urbana-Champaign); Srinivasan Parthasarathy (Department of Computer Science and Engineering, The Ohio State University); Rajiv Ramnath (Department of Computer Science and Engineering, The Ohio State University) </td></tr><tr><td><strong>LeMon: Automating Portrait Generation for Zero-Shot Story Visualization with Multi-Character Interactions<strong></strong></strong></td></tr><tr><td>Ziyi Kou (Department of Computer Science and Engineering, University of Notre Dame); Shichao Pei (Department of Computer Science, University of Massachusetts Boston); Xiangliang Zhang (Department of Computer Science and Engineering, University of Notre Dame) </td></tr><tr><td><strong>Image Similarity Using an Ensemble of Context-Sensitive Models<strong></strong></strong></td></tr><tr><td>Zukang Liao (University of Oxford); Min Chen (University of Oxford) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 11:00-13:00, Room 133</strong><br><strong>Theme: Scalable ML II</strong><br><strong>Session Chair: Da Zheng (Ant Group)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity<strong></strong></strong></td></tr><tr><td>Dongyue Li (Northeastern University); Aneesh Sharma (Google); Hongyang R. Zhang (Northeastern University) </td></tr><tr><td><strong>QSketch: An Efficient Sketch for Weighted Cardinality Estimation in Streams<strong></strong></strong></td></tr><tr><td>Yiyan Qi(International Digital Economy Academy (IDEA)); Rundong Li (MOE KLINNS Lab, Xi’an Jiaotong University); Pinghui Wang (MOE KLINNS Lab, Xi’an Jiaotong University); Yufang Sun (MOE KLINNS Lab, Xi’an Jiaotong University); Rui Xing (MOE KLINNS Lab, Shaanxi Normal University) </td></tr><tr><td><strong>Compact Decomposition of Irregular Tensors for Data Compression: From Sparse to Dense to High-Order Tensors<strong></strong></strong></td></tr><tr><td>Taehyung Kwon (KAIST); Jihoon Ko (KAIST); Jinhong Jung (Soongsil University); Jun-Gi Jang (UIUC); Kijung Shin (KAIST) </td></tr><tr><td><strong>Bivariate Decision Trees: Smaller, Interpretable, More Accurate<strong></strong></strong></td></tr><tr><td>Rasul Kairgeldin (University of California, Merced); Miguel 脕. Carreira-Perpi帽谩n (University of California, Merced) </td></tr><tr><td><strong>Scaling Training Data with Lossy Image Compression<strong></strong></strong></td></tr><tr><td>Katherine L Mentzer (Granica); Andrea Montanari (Granica) </td></tr><tr><td><strong>High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates<strong></strong></strong></td></tr><tr><td>Fred Lu (Booz Allen Hamilton, University of Maryland, Baltimore County); Ryan R. Curtin (Booz Allen Hamilton); Edward Raff (Booz Allen Hamilton, University of Maryland, Baltimore County); Francis Ferraro (University of Maryland, Baltimore County); James Holt (Laboratory for Physical Sciences) </td></tr><tr><td><strong>Distributed Thresholded Counting with Limited Interaction<strong></strong></strong></td></tr><tr><td>Xiaoyi Zhu (School of Data Science, Fudan University); Yuxiang Tian (School of Data Science, Fudan University); Zengfeng Huang (School of Data Science, Fudan University) </td></tr><tr><td><strong>ACER: Accelerating Complex Event Recognition via Two-Phase Filtering under Range Bitmap-Based Indexes<strong></strong></strong></td></tr><tr><td>Shizhe Liu (State Key Laboratory for Novel Software Technology, Nanjing University); Haipeng Dai (State Key Laboratory for Novel Software Technology, Nanjing University); Shaoxu Song (BNRist, Tsinghua University); Meng Li (State Key Laboratory for Novel Software Technology, Nanjing University); Jingsong Dai (State Key Laboratory for Novel Software Technology, Nanjing University); Rong Gu (State Key Laboratory for Novel Software Technology, Nanjing University); Guihai Chen (State Key Laboratory for Novel Software Technology, Nanjing University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 11:00-13:00, Room 133</strong><br><strong>Theme: Intelligent Education</strong><br><strong>Session Chair: Pablo Arag贸n (Wikimedia Foundation)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Leveraging Pedagogical Theories to Understand Student Learning Process with Graph-based Reasonable Knowledge Tracing<strong></strong></strong></td></tr><tr><td>Jiajun Cui (East China Normal University); Hong Qian (East China Normal University); Bo Jiang (East China Normal University); Wei Zhang (East China Normal University) </td></tr><tr><td><strong>DyGKT: Dynamic Graph Learning for Knowledge Tracing<strong></strong></strong></td></tr><tr><td>Ke Cheng (SKLSDE Lab, Beihang University); Peng Linzhi (SKLSDE Lab, Beihang University); Pengyang Wang (SKL-IOTSC, Department of Computer and Information Science, University of Macau); Junchen Ye (School of Transportation Science and Engineering, Beihang University); Leilei Sun (SKLSDE Lab, Beihang University); Bowen Du (School of Transportation Science and Engineering, Beihang University) </td></tr><tr><td><strong>RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes<strong></strong></strong></td></tr><tr><td>Xiaoshan Yu (School of Artificial Intelligence, Anhui University); Chuan Qin (Career Science Lab, BOSS Zhipin, PBC School of Finance, Tsinghua University); Dazhong Shen (Shanghai Artificial Intelligence Laboratory); Shangshang Yang (School of Artificial Intelligence, Anhui University); Haiping Ma (Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Institutes of Physical Science and Information Technology, Anhui University); Hengshu Zhu (Career Science Lab, BOSS Zhipin); Xingyi Zhang (School of Computer Science and Technology, Anhui University) </td></tr><tr><td><strong>ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems<strong></strong></strong></td></tr><tr><td>Hong Qian (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University); Shuo Liu (School of Computer Science and Technology, East China Normal University); Mingjia Li (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University); Bingdong Li (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University); Zhi Liu (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University); Aimin Zhou (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University) </td></tr><tr><td><strong>Capturing Homogeneous Influence among Students: Hypergraph Cognitive Diagnosis for Intelligent Education Systems<strong></strong></strong></td></tr><tr><td>Junhao Shen (School of Computer Science and Technology, East China Normal University); Hong Qian (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University); Shuo Liu (School of Computer Science and Technology, East China Normal University); Wei Zhang (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University); Bo Jiang (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University); Aimin Zhou (School of Computer Science and Technology, Shanghai Institute of AI Education, East China Normal University) </td></tr><tr><td><strong>Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis<strong></strong></strong></td></tr><tr><td>Dacao Zhang (School of Computer Science and Information Engineering, Hefei University of Technology); Kun Zhang (School of Computer Science and Information Engineering, Hefei University of Technology); Le Wu (School of Computer Science and Information Engineering, Hefei University of Technology, Institute of Dataspace, Hefei Comprehensive National Science Center); Mi Tian (School of Computer Science and Information Engineering, Hefei University of Technology); Richang Hong (School of Computer Science and Information Engineering, Hefei University of Technology, Institute of Dataspace, Hefei Comprehensive National Science Center); Meng Wang (School of Computer Science and Information Engineering, Hefei University of Technology) </td></tr><tr><td><strong>AdaRD: An Adaptive Response Denoising Framework for Robust Learner Modeling<strong></strong></strong></td></tr><tr><td>Fangzhou Yao (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Qi Liu (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center); Linan Yue (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Weibo Gao (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Jiatong Li (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China); Xin Li (University of Science and Technology of China, Artificial Intelligence Research Institute, iFLYTEK Co., Ltd); Yuanjing He (The Open University of China) </td></tr><tr><td><strong>Privileged Knowledge State Distillation for Reinforcement Learning-based Educational Path Recommendation<strong></strong></strong></td></tr><tr><td>Qingyao Li (Shanghai Jiao Tong University); Wei Xia (Huawei Noah’s Ark Lab); Li’ang Yin (Shanghai Jiao Tong University); Jiarui Jin (Shanghai Jiao Tong University); Yong Yu (Shanghai Jiao Tong University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 14:00-16:00, Room 116<br>Theme: Fair & Safe Recommendations</strong><br><strong>Session Chair: Stratis Ioannidis (Northeastern University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Where Have You Been? A Study of Privacy Risk for Point-of-Interest Recommendation</strong></td></tr><tr><td>Kunlin Cai (University of California, Los Angeles); Jinghuai Zhang (University of California, Los Angeles); Zhiqing Hong (Rutgers University); William Shand (University of California, Los Angeles); Guang Wang (Florida State University); Desheng Zhang (Rutgers University); Jianfeng Chi (Meta); Yuan Tian (University of California, Los Angeles)</td></tr><tr><td><strong>Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems</strong></td></tr><tr><td>Zhichen Xiang (College of Management and Economics, Tianjin University, Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University); Hongke Zhao (College of Management and Economics, Tianjin University, Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University); Chuang Zhao (The Hong Kong University of Science and Technology); Ming He (AI Lab at Lenovo Research); Jianping Fan (AI Lab at Lenovo Research)</td></tr><tr><td><strong>Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks</strong></td></tr><tr><td>Zongwei Wang (Chongqing University); Junliang Yu (The University of Queensland); Min Gao (Chongqing University); Hongzhi Yin (The University of Queensland); Bin Cui (Peking University); Shazia Sadiq (The University of Queensland)</td></tr><tr><td><strong>Debiased Recommendation with Noisy Feedback</strong></td></tr><tr><td>Haoxuan Li (Peking University); Chunyuan Zheng (University of California, San Diego); Wenjie Wang (National University of Singapore); Hao Wang (Zhejiang University); Fuli Feng (University of Science and Technology of China); Xiao-Hua Zhou (Peking University)</td></tr><tr><td><strong>A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation</strong></td></tr><tr><td>Shoujin Wang (University of Technology Sydney); Wentao Wang (University of Technology Sydney); Xiuzhen Zhang (RMIT University); Yan Wang (Macquarie University); Huan Liu (Arizona State University); Fang Chen (University of Technology Sydney (UTS))</td></tr><tr><td><strong>Harm Mitigation in Recommender Systems under User Preference Dynamics</strong></td></tr><tr><td>Jerry Chee (Cornell University); Shankar Kalyanaraman (Meta); Sindhu Kiranmai Ernala (Meta); Udi Weinsberg (Meta); Sarah Dean (Cornell University); Stratis Ioannidis (Northeastern University)</td></tr><tr><td><strong>Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time</strong></td></tr><tr><td>Haiyuan Zhao (School of Information, Renmin University of China); Guohao Cai (Noah’s Ark Lab, Huawei); Jieming Zhu (Noah’s Ark Lab, Huawei); Zhenhua Dong (Noah’s Ark Lab, Huawei); Jun Xu (Gaoling School of Artificial Intelligence, Renmin University of China); Ji-Rong Wen (Gaoling School of Artificial Intelligence, Renmin University of China)</td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 14:00-16:00, Room 120<br>Theme: Scalable Graph Mining & Learning</strong><br><strong>Session Chair: Kijung Shin (KAIST)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Fast Query of Biharmonic Distance in Networks<strong></strong></strong></td></tr><tr><td>Changan Liu (Fudan University); Ahad N. Zehmakan (Australian National University); Zhongzhi Zhang (Fudan University) </td></tr><tr><td><strong>Fast Computation of Kemeny’s Constant for Directed Graphs<strong></strong></strong></td></tr><tr><td>Haisong Xia (Fudan University); Zhongzhi Zhang (Fudan University) </td></tr><tr><td><strong>Fast Computation for the Forest Matrix of an Evolving Graph<strong></strong></strong></td></tr><tr><td>Haoxin Sun (Fudan University); Xiaotian Zhou (Fudan University); Zhongzhi Zhang (Fudan University) </td></tr><tr><td><strong>Scalable Algorithm for Finding Balanced Subgraphs with Tolerance in Signed Networks<strong></strong></strong></td></tr><tr><td>Jingbang Chen (David R. Cheriton School of Computer Science, University of Waterloo); Qiuyang Mang (School of Data Science, The Chinese University of Hong Kong, Shenzhen); Hangrui Zhou (Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University); Richard Peng (Computer Science Department, Carnegie Mellon University); Yu Gao (Independent); Chenhao Ma (School of Data Science, The Chinese University of Hong Kong, Shenzhen) </td></tr><tr><td><strong>A Fast Exact Algorithm to Enumerate Maximal Pseudo-cliques in Large Sparse Graphs<strong></strong></strong></td></tr><tr><td>Ahsanur Rahman (North South University); Kalyan Roy (North South University); Ramiza Maliha (North South University); Townim Faisal Chowdhury (Australian Institute for Machine Learning, University of Adelaide) </td></tr><tr><td><strong>Graph Condensation for Open-World Graph Learning<strong></strong></strong></td></tr><tr><td>Xinyi Gao (The University of Queensland); Tong Chen (The University of Queensland); Wentao Zhang (Peking University); Yayong Li (Data 61, CSIRO); Xiangguo Sun (The Chinese University of Hong Kong); Hongzhi Yin (The University of Queensland) </td></tr><tr><td><strong>Graph Data Condensation via Self-expressive Graph Structure Reconstruction<strong></strong></strong></td></tr><tr><td>Zhanyu Liu (Shanghai Jiao Tong University); Chaolv Zeng (Shanghai Jiao Tong University); Guanjie Zheng (Shanghai Jiao Tong University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 14:00-16:00, Room 121<br>Theme: Time Series III</strong><br><strong>Session Chair: Ravi Kumar (Google)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Robust Predictions with Ambiguous Time Delays: A Bootstrap Strategy<strong></strong></strong></td></tr><tr><td>Jiajie Wang (Changsha Research Institute of Mining and Metallurgy); Zhiyuan Jerry Lin (Meta); Wen Chen (Changsha Research Institute of Mining and Metallurgy) </td></tr><tr><td><strong>Unraveling Block Maxima Forecasting Models with Counterfactual Explanation<strong></strong></strong></td></tr><tr><td>Yue Deng (Michigan State University); Asadullah Hill Galib (Michigan State University); Pang-Ning Tan (Michigan State University); Lifeng Luo (Michigan State University) </td></tr><tr><td><strong>Quantifying and Estimating the Predictability Upper Bound of Univariate Numeric Time Series<strong></strong></strong></td></tr><tr><td>Jamal Mohammed (University of Zurich); Michael H. B枚hlen (University of Zurich); Sven Helmer (University of Zurich) </td></tr><tr><td><strong>Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data<strong></strong></strong></td></tr><tr><td>Ziyi Zhang (Texas A&M University); Shaogang Ren (Texas A&M University); Xiaoning Qian (Texas A&M University); Nick Duffield (Texas A&M University) </td></tr><tr><td><strong>Efficient Discovery of Time Series Motifs under both Length Differences and Warping<strong></strong></strong></td></tr><tr><td>Makoto Imamura (Tokai University); Takaaki Nakamura (Mitsubishi Electric Corporation) </td></tr><tr><td><strong>Semi-Supervised Learning for Time Series Collected at a Low Sampling Rate<strong></strong></strong></td></tr><tr><td>Minyoung Bae (KAIST); Yooju Shin (KAIST); Youngeun Nam (KAIST); Young Seop Lee (Samsung Electronics Co., Ltd.); Jae-Gil Lee (KAIST) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 14:00-16:00, Room 122<br>Theme: Graph Attacks</strong><br><strong>Session Chair: Lanjun Wang (Tianjin University)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Unsupervised Heterogeneous Graph Rewriting Attack via Node Clustering<strong></strong></strong></td></tr><tr><td>Haosen Wang (Southeast University, Zhejiang Lab); Can Xu (East China Normal University); Chenglong Shi (Southeast University); Pengfei Zheng (Zhejiang Lab); Shiming Zhang (University of Science and Technology of China); Minhao Cheng (Pennsylvania State University); Hongyang Chen (Zhejiang Lab) </td></tr><tr><td><strong>Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective<strong></strong></strong></td></tr><tr><td>Zhiwei Zhang (The Pennsylvania State University); Minhua Lin (The Pennsylvania State University); Enyan Dai (The Pennsylvania State University); Suhang Wang (The Pennsylvania State University) </td></tr><tr><td><strong>Cross-Context Backdoor Attacks against Graph Prompt Learning<strong></strong></strong></td></tr><tr><td>Xiaoting Lyu (School of Computer Science and Technology, Beijing Jiaotong University); Yufei Han (Inria, Univ. Rennes, IRISA); Wei Wang (Beijing Jiaotong University, Xi’an Jiaotong University); Hangwei Qian (CFAR, A*STAR); Ivor Tsang (CFAR, A*STAR); Xiangliang Zhang (University of Notre Dame) </td></tr><tr><td><strong>Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data<strong></strong></strong></td></tr><tr><td>Hanyang Yuan (Zhejiang University, Fudan University); Jiarong Xu (Fudan University); Cong Wang (Peking University); Ziqi Yang (Zhejiang University); Chunping Wang (Finvolution Group); Keting Yin (Zhejiang University); Yang Yang (Zhejiang University) </td></tr><tr><td><strong>Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Leman Go Indifferent<strong></strong></strong></td></tr><tr><td>Lorenz Kummer (Doctoral School Computer Science, University of Vienna, Faculty of Computer Science, University of Vienna); Samir Moustafa (Doctoral School Computer Science, University of Vienna, Faculty of Computer Science, University of Vienna); Sebastian Schrittwieser (Christian Doppler Laboratory for Assurance and Transparency in Software Protection, University of Vienna, Faculty of Computer Science, University of Vienna); Wilfried Gansterer (Faculty of Computer Science, University of Vienna); Nils Kriege (Research Network Data Science, University of Vienna, Faculty of Computer Science, University of Vienna) </td></tr><tr><td><strong>IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks<strong></strong></strong></td></tr><tr><td>Yushun Dong (The University of Virginia); Binchi Zhang (The University of Virginia); Zhenyu Lei (The University of Virginia); Na Zou (The University of Houston); Jundong Li (The University of Virginia) </td></tr><tr><td><strong>Certified Robustness on Visual Graph Matching via Searching Optimal Smoothing Range<strong></strong></strong></td></tr><tr><td>Huaqing Shao (Department of CSE and MoE Key Lab of AI, Shanghai Jiao Tong University); Lanjun Wang (SNMC, Tianjin University); Yongwei Wang (SIAS and College of Computer Science, Zhejiang University); Qibing Ren (Department of CSE, Shanghai Jiao Tong University); Junchi Yan (School of AI and Department of CSE, Shanghai Jiao Tong University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 14:00-16:00, Room 129-130<br>Theme: Trustworthy GNNs</strong><br><strong>Session Chair: Heng Chang (Huawei)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks<strong></strong></strong></td></tr><tr><td>Renqiang Luo (Dalian University of Technology); Huafei Huang (Dalian University of Technology); Shuo Yu (Dalian University of Technology); Zhuoyang Han (Dalian University of Technology); Estrid He (RMIT University); Xiuzhen Zhang (RMIT University); Feng Xia (RMIT University) </td></tr><tr><td><strong>One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes<strong></strong></strong></td></tr><tr><td>Yuchang Zhu (Sun Yat-Sen University); Jintang Li (Sun Yat-Sen University); Yatao Bian (Tencent AI Lab); Zibin Zheng (Sun Yat-Sen University); Liang Chen (Sun Yat-Sen University) </td></tr><tr><td><strong>Your Neighbor Matters: Towards Fair Decisions Under Networked Interference<strong></strong></strong></td></tr><tr><td>Wenjing Yang (National University of Defense Technology); Haotian Wang (National University of Defense Technology); Haoxuan Li (Peking University); Hao Zou (ZGC laboratory); Ruochun Jin (National University of Defense Technology); Kun Kuang (Zhejiang University); Peng Cui (Tsinghua University) </td></tr><tr><td><strong>Rethinking Fair Graph Neural Networks from Re-balancing<strong></strong></strong></td></tr><tr><td>Zhixun Li (The Chinese University of Hong Kong); Yushun Dong (University of Virginia); Qiang Liu (Institute of Automation, Chinese Academy of Sciences); Jeffrey Xu Yu (The Chinese University of Hong Kong) </td></tr><tr><td><strong>Balanced Confidence Calibration for Graph Neural Networks<strong></strong></strong></td></tr><tr><td>Hao Yang (National University of Defense Technology); Min Wang (National University of Defense Technology); Qi Wang (National University of Defense Technology); Mingrui Lao (National University of Defense Technology); Yun Zhou (National University of Defense Technology) </td></tr><tr><td><strong>Graph Cross Supervised Learning via Generalized Knowledge<strong></strong></strong></td></tr><tr><td>Xiangchi Yuan (Brandeis University, Georgia Institute of Technology); Yijun Tian (University of Notre Dame); Chunhui Zhang (Dartmouth College); Yanfang Ye (University of Notre Dame); Nitesh V. Chawla (University of Notre Dame); Chuxu Zhang (Brandeis University) </td></tr><tr><td><strong>Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information Theory Perspective<strong></strong></strong></td></tr><tr><td>Luying Zhong (Fuzhou University); Renjie Lin (Fuzhou University); JiayinL JiayinLi (Fujian Normal University); Shiping Wang (Fuzhou University); Zheyi Chen (Fuzhou University) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 14:00-16:00, Room 131-132<br>Theme: Finance</strong><br><strong>Session Chair: Saurabh Nagrecha (Google)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>FreQuant: A Reinforcement-Learning based Adaptive Portfolio Optimization with Multi-frequency Decomposition<strong></strong></strong></td></tr><tr><td>Jihyeong Jeon (Seoul National University); Jiwon Park (Seoul National University, DeepTrade Technologies Inc.); Chanhee Park (Seoul National University, DeepTrade Technologies Inc.); U Kang (Seoul National University) </td></tr><tr><td><strong>MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading<strong></strong></strong></td></tr><tr><td>Chuqiao Zong (Nanyang Technological University); Chaojie Wang (Skywork AI); Qin Molei (Nanyang Technological University); Lei Feng (Singapore University of Technology and Design); Xinrun Wang (Nanyang Technological University); Bo An (Nanyang Technological University) </td></tr><tr><td><strong>A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist<strong></strong></strong></td></tr><tr><td>Wentao Zhang (Nanyang Technological University); Lingxuan Zhao (Nanyang Technological University); Haochong Xia (National Technological University); Shuo Sun (Nanyang Technological University); Jiaze Sun (National University of Singapore); Molei Qin (Nanyang Technological University); Xinyi Li (Nanyang Technological University); Yuqing Zhao (Nanyang Technological University); Yilei Zhao (Zhejiang University); Xinyu Cai (Nanyang Technological University); Longtao Zheng (Nanyang Technological University); Xinrun Wang (Nanyang Technological University); Bo An (Nanyang Technological University, Skywork AI) </td></tr><tr><td><strong>Money Never Sleeps: Maximizing Liquidity Mining Yields in Decentralized Finance<strong></strong></strong></td></tr><tr><td>Wangze Ni (Hong Kong University of Science and Technology); Zhao Yiwei (Hong Kong Polytechnic University); Weijie Sun (Hong Kong University of Science and Technology); Lei Chen (Hong Kong University of Science and Technology); Peng Cheng (East China Normal University); Chen Zhang (Hong Kong Polytechnic University); Xuemin Lin (Shanghai Jiaotong University) </td></tr><tr><td><strong>Cost-Efficient Fraud Risk Optimization with Submodularity in Insurance Claim<strong></strong></strong></td></tr><tr><td>Yupeng Wu (School of Computer Science and Technology, East China Normal University); Zhibo Zhu (Ant Group); Chaoyi Ma (Ant Group); Hong Qian (School of Computer Science and Technology, East China Normal University); Xingyu Lu (Ant Group); Yangwenhui Zhang (School of Computer Science and Technology, East China Normal University); Xiaobo Qin (AntGroup); Binjie Fei (AntGroup); Jun Zhou (Ant Group); Aimin Zhou (School of Computer Science and Technology, East China Normal University) </td></tr><tr><td><strong>Dynamic Hotel Pricing at Online Travel Platforms: A Popularity and Competitiveness Aware Demand Learning Approach<strong></strong></strong></td></tr><tr><td>Fanwei Zhu (Hangzhou City University); Wendong Xiao (Alibaba Group); Yao Yu (Alibaba Group); Zemin Liu (Zhejiang University); Zulong Chen (Alibaba Group); Weibin Cai (Syracuse University) </td></tr><tr><td><strong>BitLINK: Temporal Linkage of Address Clusters in Bitcoin Blockchain<strong></strong></strong></td></tr><tr><td>Sheng Zhong (The University of New Mexico); Abdullah Mueen (The University of New Mexico) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 14:00-16:00, Room 133<br>Theme: Robust ML</strong><br><strong>Session Chair: Heitor Murilo Gomes (Victoria University of Wellington)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>Uplift Modelling via Gradient Boosting<strong></strong></strong></td></tr><tr><td>Bulat Ibragimov (Moscow Institute of Physics and Technology, Artificial Intelligence Research Institute (AIRI)); Anton Vakhrushev (Sber AI Lab) </td></tr><tr><td><strong>Learn Together Stop Apart: an Inclusive Approach to Ensemble Pruning<strong></strong></strong></td></tr><tr><td>Bulat Ibragimov (Moscow Institute of Physics and Technology, Artificial Intelligence Research Institute (AIRI)); Gleb Gusev (Sber AI Lab) </td></tr><tr><td><strong>CASH via Optimal Diversity for Ensemble Learning<strong></strong></strong></td></tr><tr><td>Pranav Poduval (MasterCard AI Garage); Sanjay Patnala (Mastercard AI Garage); Gaurav Oberoi (MasterCard AI Garage); Nitish Kumar (MasterCard AI Garage); Siddhartha Asthana (MasterCard AI Garage) </td></tr><tr><td><strong>Tackling Instance-Dependent Label Noise with Class Rebalance and Geometric Regularization<strong></strong></strong></td></tr><tr><td>Shuzhi Cao(School of Computer Science and Technology & Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University); Jianfei Ruan(School of Computer Science and Technology & Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University); Bo Dong (School of Distance Education & Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University); Bin Shi (School of Computer Science and Technology & Ministry of Education Key Lab For Intelligent Networks and Network Security, Xi’an Jiaotong University) </td></tr><tr><td><strong>Spuriousness-Aware Meta-Learning for Learning Robust Classifiers<strong></strong></strong></td></tr><tr><td>Guangtao Zheng (University of Virginia); Wenqian Ye (University of Virginia); Aidong Zhang (University of Virginia) </td></tr><tr><td><strong>Self-Distilled Disentangled Learning for Counterfactual Prediction<strong></strong></strong></td></tr><tr><td>Xinshu Li (The University of New South Wales); Mingming Gong (The University of Melbourne, MBZUAI); Lina Yao (CSIRO’s Data61, The University of New South Wales) </td></tr><tr><td><strong>RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks<strong></strong></strong></td></tr><tr><td>Seong-Hyeon Hwang (Korea Advanced Institute of Science and Technology); Minsu Kim (Korea Advanced Institute of Science & Technology); Steven Euijong Whang (Korea Advanced Institute of Science and Technology) </td></tr></tbody></table></figure> </div></div> <div class="wp-block-group"><div class="wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained"> <p class="has-text-align-center"><strong>Thursday, August 29 14:00-16:00, Room 134<br>Theme: Urban Data II</strong><br><strong>Session Chair: Andreas Kaltenbrunner (Universitat Oberta de Catalunya)</strong></p> <figure class="wp-block-table is-style-stripes"><table class="has-fixed-layout"><tbody><tr><td><strong>ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model<strong></strong></strong></td></tr><tr><td>Yuanshao Zhu (Southern University of Science and Technology, City University of Hong Kong); James Jianqiao Yu (University of York); Xiangyu Zhao (City University of Hong Kong); Qidong Liu (Xi’an Jiao Tong University, City University of Hong Kong); Yongchao Ye (City University of Hong Kong); Wei Chen (Hong Kong University of Science and Technology (Guangzhou)); Zijian Zhang (Jilin University, City University of Hong Kong); Xuetao Wei (Southern University of Science and Technology); Yuxuan Liang (The Hong Kong University of Science and Technology (Guangzhou)) </td></tr><tr><td><strong>Communication-efficient Multi-service Mobile Traffic Prediction by Leveraging Cross-service Correlations<strong></strong></strong></td></tr><tr><td>Zhiying Feng (School of Computer Science and Engineering, Sun Yat-sen University); Qiong Wu (Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology); Xu Chen (School of Computer Science and Engineering, Sun Yat-sen University) </td></tr><tr><td><strong>Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks<strong></strong></strong></td></tr><tr><td>Weijia Zhang (HKUST(GZ)); Le Zhang(Baidu Research); Jindong Han (HKUST); Hao Liu (HKUST(GZ), HKUST); Yanjie Fu (Arizona State University); Jingbo Zhou (Baidu Research); Yu Mei(Baidu Inc.); Hui Xiong (HKUST(GZ), HKUST) </td></tr><tr><td><strong>ITPNet: Towards Instantaneous Trajectory Prediction for Autonomous Driving<strong></strong></strong></td></tr><tr><td>Rongqing Li (Beijing Institute of Technology); Changsheng Li (Beijing Institute of Technology); Yuhang Li (Beijing Institute of Technology); Hanjie Li (Beijing Institute of Technology); Yi Chen (Beijing Institute of Technology); Ye Yuan (Beijing Institute of Technology); Guoren Wang (Beijing Institute of Technology) </td></tr><tr><td><strong>Profiling Urban Streets: A Semi-Supervised Prediction Model Based on Street View Imagery and Spatial Topology<strong></strong></strong></td></tr><tr><td>Meng Chen (School of Software, Shandong University); Zechen Li (School of Software, Shandong University); Weiming Huang (School of Computer Science and Engineering, Nanyang Technological University); Yongshun Gong (School of Software, Shandong University); Yilong Yin (School of Software, Shandong University) </td></tr><tr><td><strong>Physics-informed Neural ODE for Post-disaster Mobility Recovery<strong></strong></strong></td></tr><tr><td>Jiahao Li (Shenzhen International Graduate School, Tsinghua University); Huandong Wang (Department of Electronic Engineering, Tsinghua University); Xinlei Chen (Shenzhen International Graduate School, Tsinghua University, Pengcheng Laboratory) </td></tr><tr><td><strong>DiffCrime: A Multimodal Conditional Diffusion Model for Crime Risk Map Inference<strong></strong></strong></td></tr><tr><td>Shuliang Wang (Beijing Institute of Technology); Xinyu Pan (Beijing Institute of Technology); Sijie Ruan (Beijing Institute of Technology); Haoyu Han (Beijing Institute of Technology); Ziyu Wang (Beijing Institute of Technology); Hanning Yuan (Beijing Institute of Technology); Jiabao Zhu (Beijing Institute of Technology); Qi Li (Beijing Institute of Technology) </td></tr></tbody></table></figure> </div></div> <div class="post-tags"> </div> </div> </main> <footer id="site-footer" class="site-footer dynamic-footer footer-has-copyright"> <div class="footer-inner"> <div class="site-branding show-logo"> <div class="site-logo hide"> <a href="https://kdd2024.kdd.org/" class="custom-logo-link" rel="home"><img fetchpriority="high" width="640" height="144" src="https://kdd2024.kdd.org/wp-content/uploads/2024/01/kdd24-logo-small.jpeg" class="custom-logo" alt="ACM KDD 2024" decoding="async" srcset="https://kdd2024.kdd.org/wp-content/uploads/2024/01/kdd24-logo-small.jpeg 640w, https://kdd2024.kdd.org/wp-content/uploads/2024/01/kdd24-logo-small-300x68.jpeg 300w" sizes="(max-width: 640px) 100vw, 640px" /></a> </div> <p class="site-description show"> KDD 2024 | Barcelona, Spain </p> </div> <div class="copyright show"> <p>All rights reserved</p> </div> </div> </footer> <script src="https://kdd2024.kdd.org/wp-content/themes/hello-elementor/assets/js/hello-frontend.min.js?ver=3.0.0" id="hello-theme-frontend-js"></script> </body> </html>