Machine Learning Feb 2022

<!DOCTYPE html> <html lang="en"> <head> <title>Machine Learning Feb 2022</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20241206" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link 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<a href=/list/cs.LG/2022-02?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2022-02?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2022-02?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/2202.00004" title="Abstract" id="2202.00004"> arXiv:2202.00004 </a> [<a href="/pdf/2202.00004" title="Download PDF" id="pdf-2202.00004" aria-labelledby="pdf-2202.00004">pdf</a>, <a href="/format/2202.00004" title="Other formats" id="oth-2202.00004" aria-labelledby="oth-2202.00004">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On Polynomial Approximation of Activation Function </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chiang,+J">John Chiang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> In this work, we proposed an interesting method to approximate the activation function by a polynomial the degree of which is preset low. Our method to approximate the activation function is much more flexible compared to the least square method in the sense that the additional parameters could better control the shape of the resulting polynomial to approximate </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Cryptography and Security (cs.CR) </div> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2202.00009" title="Abstract" id="2202.00009"> arXiv:2202.00009 </a> [<a href="/pdf/2202.00009" title="Download PDF" id="pdf-2202.00009" aria-labelledby="pdf-2202.00009">pdf</a>, <a href="/format/2202.00009" title="Other formats" id="oth-2202.00009" aria-labelledby="oth-2202.00009">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Identifying Dementia Subtypes with Electronic Health Records </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kumar,+S">Sayantan Kumar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abrams,+Z">Zachary Abrams</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schindler,+S">Suzanne Schindler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ghoshal,+N">Nupur Ghoshal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Payne,+P">Philip Payne</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ACM Conference on Bioinformatics, Computational Biology, and Health Informatics 13 pages, 7 figures, 3 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2202.00035" title="Abstract" id="2202.00035"> arXiv:2202.00035 </a> [<a href="/pdf/2202.00035" title="Download PDF" id="pdf-2202.00035" aria-labelledby="pdf-2202.00035">pdf</a>, <a href="/format/2202.00035" title="Other formats" id="oth-2202.00035" aria-labelledby="oth-2202.00035">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Fair Representations via Rate-Distortion Maximization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chowdhury,+S+B+R">Somnath Basu Roy Chowdhury</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chaturvedi,+S">Snigdha Chaturvedi</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at TACL </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computation and Language (cs.CL) </div> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2202.00045" title="Abstract" id="2202.00045"> arXiv:2202.00045 </a> [<a href="/pdf/2202.00045" title="Download PDF" id="pdf-2202.00045" aria-labelledby="pdf-2202.00045">pdf</a>, <a href="/format/2202.00045" title="Other formats" id="oth-2202.00045" aria-labelledby="oth-2202.00045">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Alkhatib,+N">Natasha Alkhatib</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mushtaq,+M">Maria Mushtaq</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ghauch,+H">Hadi Ghauch</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Danger,+J">Jean-Luc Danger</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI) </div> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2202.00060" title="Abstract" id="2202.00060"> arXiv:2202.00060 </a> [<a href="/pdf/2202.00060" title="Download PDF" id="pdf-2202.00060" aria-labelledby="pdf-2202.00060">pdf</a>, <a href="/format/2202.00060" title="Other formats" id="oth-2202.00060" aria-labelledby="oth-2202.00060">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> SnAKe: Bayesian Optimization with Pathwise Exploration </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Folch,+J+P">Jose Pablo Folch</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+S">Shiqiang Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+R+M">Robert M Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shafei,+B">Behrang Shafei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Walz,+D">David Walz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tsay,+C">Calvin Tsay</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=van+der+Wilk,+M">Mark van der Wilk</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Misener,+R">Ruth Misener</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 10 main pages, 39 with appendix, 30 figures, 10 tables. Final camera-ready version for NeurIPS, with supplementary material included </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2202.00063" title="Abstract" id="2202.00063"> arXiv:2202.00063 </a> [<a href="/pdf/2202.00063" title="Download PDF" id="pdf-2202.00063" aria-labelledby="pdf-2202.00063">pdf</a>, <a href="/format/2202.00063" title="Other formats" id="oth-2202.00063" aria-labelledby="oth-2202.00063">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+X">Xuezhou Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Song,+Y">Yuda Song</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Uehara,+M">Masatoshi Uehara</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+M">Mengdi Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Agarwal,+A">Alekh Agarwal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+W">Wen Sun</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2202.00070" title="Abstract" id="2202.00070"> arXiv:2202.00070 </a> [<a href="/pdf/2202.00070" title="Download PDF" id="pdf-2202.00070" aria-labelledby="pdf-2202.00070">pdf</a>, <a href="/format/2202.00070" title="Other formats" id="oth-2202.00070" aria-labelledby="oth-2202.00070">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Implicit Concept Drift Detection for Multi-label Data Streams </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gulcan,+E+B">Ege Berkay Gulcan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Can,+F">Fazli Can</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 18 pages, 7 figures, submitted to Artificial Intelligence Review </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Information Retrieval (cs.IR) </div> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2202.00071" title="Abstract" id="2202.00071"> arXiv:2202.00071 </a> [<a href="/pdf/2202.00071" title="Download PDF" id="pdf-2202.00071" aria-labelledby="pdf-2202.00071">pdf</a>, <a href="/format/2202.00071" title="Other formats" id="oth-2202.00071" aria-labelledby="oth-2202.00071">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Qian,+C">Cheng Qian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+K">Kejun Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Glass,+L">Lucas Glass</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=S.,+R">Rakshith S. Srinivasa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+J">Jimeng Sun</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Information Retrieval (cs.IR); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2202.00075" title="Abstract" id="2202.00075"> arXiv:2202.00075 </a> [<a href="/pdf/2202.00075" title="Download PDF" id="pdf-2202.00075" aria-labelledby="pdf-2202.00075">pdf</a>, <a href="/format/2202.00075" title="Other formats" id="oth-2202.00075" aria-labelledby="oth-2202.00075">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xue,+Z">Zihui Xue</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+Y">Yuedong Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+M">Mengtian Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Marculescu,+R">Radu Marculescu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2202.00079" title="Abstract" id="2202.00079"> arXiv:2202.00079 </a> [<a href="/pdf/2202.00079" title="Download PDF" id="pdf-2202.00079" aria-labelledby="pdf-2202.00079">pdf</a>, <a href="/format/2202.00079" title="Other formats" id="oth-2202.00079" aria-labelledby="oth-2202.00079">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> You May Not Need Ratio Clipping in PPO </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+M">Mingfei Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kurin,+V">Vitaly Kurin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+G">Guoqing Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Devlin,+S">Sam Devlin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qin,+T">Tao Qin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hofmann,+K">Katja Hofmann</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Whiteson,+S">Shimon Whiteson</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/2202.00082" title="Abstract" id="2202.00082"> arXiv:2202.00082 </a> [<a href="/pdf/2202.00082" title="Download PDF" id="pdf-2202.00082" aria-labelledby="pdf-2202.00082">pdf</a>, <a href="/format/2202.00082" title="Other formats" id="oth-2202.00082" aria-labelledby="oth-2202.00082">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Trust Region Bounds for Decentralized PPO Under Non-stationarity </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+M">Mingfei Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Devlin,+S">Sam Devlin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Beck,+J">Jacob Beck</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hofmann,+K">Katja Hofmann</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Whiteson,+S">Shimon Whiteson</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> AAMAS 2023 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2202.00088" title="Abstract" id="2202.00088"> arXiv:2202.00088 </a> [<a href="/pdf/2202.00088" title="Download PDF" id="pdf-2202.00088" aria-labelledby="pdf-2202.00088">pdf</a>, <a href="/format/2202.00088" title="Other formats" id="oth-2202.00088" aria-labelledby="oth-2202.00088">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Reinforcement Learning with Heterogeneous Data: Estimation and Inference </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+E+Y">Elynn Y. Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Song,+R">Rui Song</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jordan,+M+I">Michael I. Jordan</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Methodology (stat.ME) </div> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2202.00089" title="Abstract" id="2202.00089"> arXiv:2202.00089 </a> [<a href="/pdf/2202.00089" title="Download PDF" id="pdf-2202.00089" aria-labelledby="pdf-2202.00089">pdf</a>, <a href="/format/2202.00089" title="Other formats" id="oth-2202.00089" aria-labelledby="oth-2202.00089">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Understanding AdamW through Proximal Methods and Scale-Freeness </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhuang,+Z">Zhenxun Zhuang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+M">Mingrui Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cutkosky,+A">Ashok Cutkosky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Orabona,+F">Francesco Orabona</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2202.00091" title="Abstract" id="2202.00091"> arXiv:2202.00091 </a> [<a href="/pdf/2202.00091" title="Download PDF" id="pdf-2202.00091" aria-labelledby="pdf-2202.00091">pdf</a>, <a href="/format/2202.00091" title="Other formats" id="oth-2202.00091" aria-labelledby="oth-2202.00091">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Vo,+V+Q">Viet Quoc Vo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abbasnejad,+E">Ehsan Abbasnejad</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ranasinghe,+D+C">Damith C. Ranasinghe</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Published as a conference paper at the International Conference on Learning Representations (ICLR 2022). Code is available at <a href="https://sparseevoattack.github.io/" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2202.00097" title="Abstract" id="2202.00097"> arXiv:2202.00097 </a> [<a href="/pdf/2202.00097" title="Download PDF" id="pdf-2202.00097" aria-labelledby="pdf-2202.00097">pdf</a>, <a href="/format/2202.00097" title="Other formats" id="oth-2202.00097" aria-labelledby="oth-2202.00097">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Shirian,+A">Amir Shirian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Somandepalli,+K">Krishna Somandepalli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guha,+T">Tanaya Guha</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Sound (cs.SD) </div> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2202.00104" title="Abstract" id="2202.00104"> arXiv:2202.00104 </a> [<a href="/pdf/2202.00104" title="Download PDF" id="pdf-2202.00104" aria-labelledby="pdf-2202.00104">pdf</a>, <a href="/format/2202.00104" title="Other formats" id="oth-2202.00104" aria-labelledby="oth-2202.00104">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Generalization in Cooperative Multi-Agent Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mahajan,+A">Anuj Mahajan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Samvelyan,+M">Mikayel Samvelyan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gupta,+T">Tarun Gupta</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ellis,+B">Benjamin Ellis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+M">Mingfei Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rockt%C3%A4schel,+T">Tim Rockt盲schel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Whiteson,+S">Shimon Whiteson</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) </div> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2202.00113" title="Abstract" id="2202.00113"> arXiv:2202.00113 </a> [<a href="/pdf/2202.00113" title="Download PDF" id="pdf-2202.00113" aria-labelledby="pdf-2202.00113">pdf</a>, <a href="/format/2202.00113" title="Other formats" id="oth-2202.00113" aria-labelledby="oth-2202.00113">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Imbedding Deep Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Corbett,+A">Andrew Corbett</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kangin,+D">Dmitry Kangin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted as a spotlight paper at the 10th International Conference on Learning Representations (ICLR), 2022 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2202.00117" title="Abstract" id="2202.00117"> arXiv:2202.00117 </a> [<a href="/pdf/2202.00117" title="Download PDF" id="pdf-2202.00117" aria-labelledby="pdf-2202.00117">pdf</a>, <a href="/format/2202.00117" title="Other formats" id="oth-2202.00117" aria-labelledby="oth-2202.00117">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Continuous Forecasting via Neural Eigen Decomposition </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Belogolovsky,+S">Stav Belogolovsky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Greenberg,+I">Ido Greenberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Eitan,+D">Danny Eitan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mannor,+S">Shie Mannor</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Systems and Control (eess.SY) </div> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/2202.00132" title="Abstract" id="2202.00132"> arXiv:2202.00132 </a> [<a href="/pdf/2202.00132" title="Download PDF" id="pdf-2202.00132" aria-labelledby="pdf-2202.00132">pdf</a>, <a href="/format/2202.00132" title="Other formats" id="oth-2202.00132" aria-labelledby="oth-2202.00132">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Submodularity In Machine Learning and Artificial Intelligence </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bilmes,+J">Jeff Bilmes</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2202.00145" title="Abstract" id="2202.00145"> arXiv:2202.00145 </a> [<a href="/pdf/2202.00145" title="Download PDF" id="pdf-2202.00145" aria-labelledby="pdf-2202.00145">pdf</a>, <a href="/format/2202.00145" title="Other formats" id="oth-2202.00145" aria-labelledby="oth-2202.00145">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Step-size Adaptation Using Exponentiated Gradient Updates </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Amid,+E">Ehsan Amid</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Anil,+R">Rohan Anil</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fifty,+C">Christopher Fifty</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Warmuth,+M+K">Manfred K. Warmuth</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2202.00146" title="Abstract" id="2202.00146"> arXiv:2202.00146 </a> [<a href="/pdf/2202.00146" title="Download PDF" id="pdf-2202.00146" aria-labelledby="pdf-2202.00146">pdf</a>, <a href="/format/2202.00146" title="Other formats" id="oth-2202.00146" aria-labelledby="oth-2202.00146">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kocherzhenko,+A+A">Aleksey A. Kocherzhenko</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kartha,+N+S">Nirmal Sobha Kartha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+T">Tengfei Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hsin-Yi">Hsin-Yi</a> (Jenny)<a href="https://arxiv.org/search/cs?searchtype=author&query=Shih">Shih</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mandic,+M">Marco Mandic</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fuller,+M">Mike Fuller</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Navruzyan,+A">Arshak Navruzyan</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2202.00150" title="Abstract" id="2202.00150"> arXiv:2202.00150 </a> [<a href="/pdf/2202.00150" title="Download PDF" id="pdf-2202.00150" aria-labelledby="pdf-2202.00150">pdf</a>, <a href="/format/2202.00150" title="Other formats" id="oth-2202.00150" aria-labelledby="oth-2202.00150">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Infinite-Horizon Average-Reward Markov Decision Processes with Constraints </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+L">Liyu Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jain,+R">Rahul Jain</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Luo,+H">Haipeng Luo</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2202.00153" title="Abstract" id="2202.00153"> arXiv:2202.00153 </a> [<a href="/pdf/2202.00153" title="Download PDF" id="pdf-2202.00153" aria-labelledby="pdf-2202.00153">pdf</a>, <a href="/format/2202.00153" title="Other formats" id="oth-2202.00153" aria-labelledby="oth-2202.00153">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Transformer-based Models of Text Normalization for Speech Applications </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ro,+J+H">Jae Hun Ro</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stahlberg,+F">Felix Stahlberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+K">Ke Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kumar,+S">Shankar Kumar</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2202.00155" title="Abstract" id="2202.00155"> arXiv:2202.00155 </a> [<a href="/pdf/2202.00155" title="Download PDF" id="pdf-2202.00155" aria-labelledby="pdf-2202.00155">pdf</a>, <a href="/format/2202.00155" title="Other formats" id="oth-2202.00155" aria-labelledby="oth-2202.00155">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fortuitous Forgetting in Connectionist Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+H">Hattie Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vani,+A">Ankit Vani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Larochelle,+H">Hugo Larochelle</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Courville,+A">Aaron Courville</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICLR Camera Ready </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> ICLR 2022 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) </div> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2202.00161" title="Abstract" id="2202.00161"> arXiv:2202.00161 </a> [<a href="/pdf/2202.00161" title="Download PDF" id="pdf-2202.00161" aria-labelledby="pdf-2202.00161">pdf</a>, <a href="/format/2202.00161" title="Other formats" id="oth-2202.00161" aria-labelledby="oth-2202.00161">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Laskin,+M">Michael Laskin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+H">Hao Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Peng,+X+B">Xue Bin Peng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yarats,+D">Denis Yarats</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rajeswaran,+A">Aravind Rajeswaran</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abbeel,+P">Pieter Abbeel</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Project website: <a href="https://sites.google.com/view/cicrl/" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2202.00195" title="Abstract" id="2202.00195"> arXiv:2202.00195 </a> [<a href="/pdf/2202.00195" title="Download PDF" id="pdf-2202.00195" aria-labelledby="pdf-2202.00195">pdf</a>, <a href="/format/2202.00195" title="Other formats" id="oth-2202.00195" aria-labelledby="oth-2202.00195">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ahn,+J">Jin-Hyun Ahn</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+K">Kyungsang Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Koh,+J">Jeongwan Koh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Q">Quanzheng Li</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 13 pages, 9 figures, submitted for conference publication </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2202.00211" title="Abstract" id="2202.00211"> arXiv:2202.00211 </a> [<a href="/pdf/2202.00211" title="Download PDF" id="pdf-2202.00211" aria-labelledby="pdf-2202.00211">pdf</a>, <a href="/format/2202.00211" title="Other formats" id="oth-2202.00211" aria-labelledby="oth-2202.00211">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=He,+Y">Yixuan He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gan,+Q">Quan Gan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wipf,+D">David Wipf</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Reinert,+G">Gesine Reinert</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yan,+J">Junchi Yan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cucuringu,+M">Mihai Cucuringu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICML 2022 spotlight; 32 pages (9 pages for main text) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2202.00255" title="Abstract" id="2202.00255"> arXiv:2202.00255 </a> [<a href="/pdf/2202.00255" title="Download PDF" id="pdf-2202.00255" aria-labelledby="pdf-2202.00255">pdf</a>, <a href="/format/2202.00255" title="Other formats" id="oth-2202.00255" aria-labelledby="oth-2202.00255">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yau,+C">Chung-Yiu Yau</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wai,+H">Hoi-To Wai</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at TMLR, 41 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Distributed, Parallel, and Cluster Computing (cs.DC); Data Structures and Algorithms (cs.DS); Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2202.00263" title="Abstract" id="2202.00263"> arXiv:2202.00263 </a> [<a href="/pdf/2202.00263" title="Download PDF" id="pdf-2202.00263" aria-labelledby="pdf-2202.00263">pdf</a>, <a href="/format/2202.00263" title="Other formats" id="oth-2202.00263" aria-labelledby="oth-2202.00263">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fully Online Meta-Learning Without Task Boundaries </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Rajasegaran,+J">Jathushan Rajasegaran</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Finn,+C">Chelsea Finn</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Levine,+S">Sergey Levine</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/2202.00264" title="Abstract" id="2202.00264"> arXiv:2202.00264 </a> [<a href="/pdf/2202.00264" title="Download PDF" id="pdf-2202.00264" aria-labelledby="pdf-2202.00264">pdf</a>, <a href="/format/2202.00264" title="Other formats" id="oth-2202.00264" aria-labelledby="oth-2202.00264">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Graph-based Neural Acceleration for Nonnegative Matrix Factorization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sj%C3%B6lund,+J">Jens Sj枚lund</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=B%C3%A5nkestad,+M">Maria B氓nkestad</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Authors contributed equally </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Numerical Analysis (math.NA); Optimization and Control (math.OC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/2202.00270" title="Abstract" id="2202.00270"> arXiv:2202.00270 </a> [<a href="/pdf/2202.00270" title="Download PDF" id="pdf-2202.00270" aria-labelledby="pdf-2202.00270">pdf</a>, <a href="/format/2202.00270" title="Other formats" id="oth-2202.00270" aria-labelledby="oth-2202.00270">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Jeong,+W">Wonyong Jeong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hwang,+S+J">Sung Ju Hwang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/2202.00273" title="Abstract" id="2202.00273"> arXiv:2202.00273 </a> [<a href="/pdf/2202.00273" title="Download PDF" id="pdf-2202.00273" aria-labelledby="pdf-2202.00273">pdf</a>, <a href="/format/2202.00273" title="Other formats" id="oth-2202.00273" aria-labelledby="oth-2202.00273">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sauer,+A">Axel Sauer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schwarz,+K">Katja Schwarz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Geiger,+A">Andreas Geiger</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To appear in SIGGRAPH 2022. Project Page: <a href="https://sites.google.com/view/stylegan-xl/" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2202.00275" title="Abstract" id="2202.00275"> arXiv:2202.00275 </a> [<a href="/pdf/2202.00275" title="Download PDF" id="pdf-2202.00275" aria-labelledby="pdf-2202.00275">pdf</a>, <a href="/format/2202.00275" title="Other formats" id="oth-2202.00275" aria-labelledby="oth-2202.00275">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Architecture Matters in Continual Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mirzadeh,+S+I">Seyed Iman Mirzadeh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chaudhry,+A">Arslan Chaudhry</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yin,+D">Dong Yin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nguyen,+T">Timothy Nguyen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pascanu,+R">Razvan Pascanu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gorur,+D">Dilan Gorur</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Farajtabar,+M">Mehrdad Farajtabar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> preprint </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2202.00280" title="Abstract" id="2202.00280"> arXiv:2202.00280 </a> [<a href="/pdf/2202.00280" title="Download PDF" id="pdf-2202.00280" aria-labelledby="pdf-2202.00280">pdf</a>, <a href="/format/2202.00280" title="Other formats" id="oth-2202.00280" aria-labelledby="oth-2202.00280">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Azam,+S+S">Sheikh Shams Azam</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hosseinalipour,+S">Seyyedali Hosseinalipour</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qiu,+Q">Qiang Qiu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Brinton,+C">Christopher Brinton</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> In Proceedings of the 10th International Conference on Learning Representations (ICLR) 2022 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item35'>[35]</a> <a href ="/abs/2202.00299" title="Abstract" id="2202.00299"> arXiv:2202.00299 </a> [<a href="/pdf/2202.00299" title="Download PDF" id="pdf-2202.00299" aria-labelledby="pdf-2202.00299">pdf</a>, <a href="/format/2202.00299" title="Other formats" id="oth-2202.00299" aria-labelledby="oth-2202.00299">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Physics-Consistent Particle Interactions </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+Z">Zhichao Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kammer,+D+S">David S. Kammer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fink,+O">Olga Fink</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Under review. 19 pages main content + 18 pages SI. Links of supporting code and data can be found at the end of main content </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computational Physics (physics.comp-ph) </div> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2202.00308" title="Abstract" id="2202.00308"> arXiv:2202.00308 </a> [<a href="/pdf/2202.00308" title="Download PDF" id="pdf-2202.00308" aria-labelledby="pdf-2202.00308">pdf</a>, <a href="/format/2202.00308" title="Other formats" id="oth-2202.00308" aria-labelledby="oth-2202.00308">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gargiani,+M">Matilde Gargiani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zanelli,+A">Andrea Zanelli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Martinelli,+A">Andrea Martinelli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Summers,+T">Tyler Summers</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lygeros,+J">John Lygeros</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/2202.00339" title="Abstract" id="2202.00339"> arXiv:2202.00339 </a> [<a href="/pdf/2202.00339" title="Download PDF" id="pdf-2202.00339" aria-labelledby="pdf-2202.00339">pdf</a>, <a href="/format/2202.00339" title="Other formats" id="oth-2202.00339" aria-labelledby="oth-2202.00339">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Quantifying Relevance in Learning and Inference </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Marsili,+M">Matteo Marsili</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Roudi,+Y">Yasser Roudi</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> review article, 63 pages, 14 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Disordered Systems and Neural Networks (cond-mat.dis-nn); Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item38'>[38]</a> <a href ="/abs/2202.00345" title="Abstract" id="2202.00345"> arXiv:2202.00345 </a> [<a href="/pdf/2202.00345" title="Download PDF" id="pdf-2202.00345" aria-labelledby="pdf-2202.00345">pdf</a>, <a href="/format/2202.00345" title="Other formats" id="oth-2202.00345" aria-labelledby="oth-2202.00345">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Exploring layerwise decision making in DNNs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mouton,+C">Coenraad Mouton</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Davel,+M+H">Marelie H. Davel</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> In Communications in Computer and Information Science, vol 1551. Springer, Cham (2022) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/2202.00386" title="Abstract" id="2202.00386"> arXiv:2202.00386 </a> [<a href="/pdf/2202.00386" title="Download PDF" id="pdf-2202.00386" aria-labelledby="pdf-2202.00386">pdf</a>, <a href="/format/2202.00386" title="Other formats" id="oth-2202.00386" aria-labelledby="oth-2202.00386">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Aggarwal,+U">Umang Aggarwal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Popescu,+A">Adrian Popescu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Belouadah,+E">Eden Belouadah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hudelot,+C">C茅line Hudelot</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Multimedia Tools and Applications (2021) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item40'>[40]</a> <a href ="/abs/2202.00390" title="Abstract" id="2202.00390"> arXiv:2202.00390 </a> [<a href="/pdf/2202.00390" title="Download PDF" id="pdf-2202.00390" aria-labelledby="pdf-2202.00390">pdf</a>, <a href="/format/2202.00390" title="Other formats" id="oth-2202.00390" aria-labelledby="oth-2202.00390">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Minority Class Oriented Active Learning for Imbalanced Datasets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Aggarwal,+U">Umang Aggarwal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Popescu,+A">Adrian Popescu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hudelot,+C">C茅line Hudelot</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> 2020 25th International Conference on Pattern Recognition (ICPR) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item41'>[41]</a> <a href ="/abs/2202.00391" title="Abstract" id="2202.00391"> arXiv:2202.00391 </a> [<a href="/pdf/2202.00391" title="Download PDF" id="pdf-2202.00391" aria-labelledby="pdf-2202.00391">pdf</a>, <a href="/format/2202.00391" title="Other formats" id="oth-2202.00391" aria-labelledby="oth-2202.00391">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Shao,+X">Xiaoting Shao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stelzner,+K">Karl Stelzner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kersting,+K">Kristian Kersting</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item42'>[42]</a> <a href ="/abs/2202.00395" title="Abstract" id="2202.00395"> arXiv:2202.00395 </a> [<a href="/pdf/2202.00395" title="Download PDF" id="pdf-2202.00395" aria-labelledby="pdf-2202.00395">pdf</a>, <a href="/format/2202.00395" title="Other formats" id="oth-2202.00395" aria-labelledby="oth-2202.00395">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ishida,+T">Takashi Ishida</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yamane,+I">Ikko Yamane</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Charoenphakdee,+N">Nontawat Charoenphakdee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Niu,+G">Gang Niu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sugiyama,+M">Masashi Sugiyama</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICLR 2023 (notable-top-5%) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2202.00408" title="Abstract" id="2202.00408"> arXiv:2202.00408 </a> [<a href="/pdf/2202.00408" title="Download PDF" id="pdf-2202.00408" aria-labelledby="pdf-2202.00408">pdf</a>, <a href="/format/2202.00408" title="Other formats" id="oth-2202.00408" aria-labelledby="oth-2202.00408">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Dimensionality Reduction Meets Message Passing for Graph Node Embeddings </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sadowski,+K">Krzysztof Sadowski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Szarmach,+M">Micha艂 Szarmach</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mattia,+E">Eddie Mattia</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Changed colors in figures 3 and 5 to match the others </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item44'>[44]</a> <a href ="/abs/2202.00423" title="Abstract" id="2202.00423"> arXiv:2202.00423 </a> [<a href="/pdf/2202.00423" title="Download PDF" id="pdf-2202.00423" aria-labelledby="pdf-2202.00423">pdf</a>, <a href="/format/2202.00423" title="Other formats" id="oth-2202.00423" aria-labelledby="oth-2202.00423">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Memory-based Message Passing: Decoupling the Message for Propogation from Discrimination </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+J">Jie Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+W">Weiqi Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pu,+J">Jian Pu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted by ICASSP 2022 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2202.00441" title="Abstract" id="2202.00441"> arXiv:2202.00441 </a> [<a href="/pdf/2202.00441" title="Download PDF" id="pdf-2202.00441" aria-labelledby="pdf-2202.00441">pdf</a>, <a href="/format/2202.00441" title="Other formats" id="oth-2202.00441" aria-labelledby="oth-2202.00441">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Novikov,+G">Georgii Novikov</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bershatsky,+D">Daniel Bershatsky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gusak,+J">Julia Gusak</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shonenkov,+A">Alex Shonenkov</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dimitrov,+D">Denis Dimitrov</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Oseledets,+I">Ivan Oseledets</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2202.00450" title="Abstract" id="2202.00450"> arXiv:2202.00450 </a> [<a href="/pdf/2202.00450" title="Download PDF" id="pdf-2202.00450" aria-labelledby="pdf-2202.00450">pdf</a>, <a href="/format/2202.00450" title="Other formats" id="oth-2202.00450" aria-labelledby="oth-2202.00450">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Approximation of Images via Generalized Higher Order Singular Value Decomposition over Finite-dimensional Commutative Semisimple Algebra </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Liao,+L">Liang Liao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+S">Sen Lin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+L">Lun Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+X">Xiuwei Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+S">Song Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yan Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+X">Xinqiang Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+Q">Qi Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J">Jingyu Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 21 pages, 11 figures, several typos in the appendix corrected </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Information Theory (cs.IT); Commutative Algebra (math.AC); Representation Theory (math.RT); Spectral Theory (math.SP) </div> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/2202.00458" title="Abstract" id="2202.00458"> arXiv:2202.00458 </a> [<a href="/pdf/2202.00458" title="Download PDF" id="pdf-2202.00458" aria-labelledby="pdf-2202.00458">pdf</a>, <a href="/format/2202.00458" title="Other formats" id="oth-2202.00458" aria-labelledby="oth-2202.00458">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Machine learning to assess relatedness: the advantage of using firm-level data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Albora,+G">Giambattista Albora</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zaccaria,+A">Andrea Zaccaria</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages, 4 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item48'>[48]</a> <a href ="/abs/2202.00504" title="Abstract" id="2202.00504"> arXiv:2202.00504 </a> [<a href="/pdf/2202.00504" title="Download PDF" id="pdf-2202.00504" aria-labelledby="pdf-2202.00504">pdf</a>, <a href="/format/2202.00504" title="Other formats" id="oth-2202.00504" aria-labelledby="oth-2202.00504">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fine-grained differentiable physics: a yarn-level model for fabrics </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gong,+D">Deshan Gong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhu,+Z">Zhanxing Zhu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=J.Bulpitt,+A">Andrew J.Bulpitt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+H">He Wang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item49'>[49]</a> <a href ="/abs/2202.00512" title="Abstract" id="2202.00512"> arXiv:2202.00512 </a> [<a href="/pdf/2202.00512" title="Download PDF" id="pdf-2202.00512" aria-labelledby="pdf-2202.00512">pdf</a>, <a href="/format/2202.00512" title="Other formats" id="oth-2202.00512" aria-labelledby="oth-2202.00512">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Progressive Distillation for Fast Sampling of Diffusion Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Salimans,+T">Tim Salimans</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ho,+J">Jonathan Ho</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Published as a conference paper at ICLR 2022 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item50'>[50]</a> <a href ="/abs/2202.00517" title="Abstract" id="2202.00517"> arXiv:2202.00517 </a> [<a href="/pdf/2202.00517" title="Download PDF" id="pdf-2202.00517" aria-labelledby="pdf-2202.00517">pdf</a>, <a href="/format/2202.00517" title="Other formats" id="oth-2202.00517" aria-labelledby="oth-2202.00517">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Empirical complexity of comparator-based nearest neighbor descent </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Baron,+J+D">Jacob D. Baron</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Darling,+R+W+R">R. W. R. Darling</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages, 1 figure </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> </dl> <div class='paging'>Total of 2489 entries : <span>1-50</span> <a href=/list/cs.LG/2022-02?skip=50&show=50>51-100</a> <a href=/list/cs.LG/2022-02?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2022-02?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2022-02?skip=2450&show=50>2451-2489</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2022-02?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2022-02?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2022-02?skip=0&show=2000 rel="nofollow"> all</a> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">  <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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