Machine Learning Nov 2020

<!DOCTYPE html> <html lang="en"> <head> <title>Machine Learning Nov 2020</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/2020-11?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2020-11?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2020-11?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/2011.00050" title="Abstract" id="2011.00050"> arXiv:2011.00050 </a> [<a href="/pdf/2011.00050" title="Download PDF" id="pdf-2011.00050" aria-labelledby="pdf-2011.00050">pdf</a>, <a href="/format/2011.00050" title="Other formats" id="oth-2011.00050" aria-labelledby="oth-2011.00050">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Dataset Meta-Learning from Kernel Ridge-Regression </div> <div class='list-authors'><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=Chen,+Z">Zhourong Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+J">Jaehoon Lee</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to ICLR 2021. Open source implementation: <a href="https://colab.sandbox.google.com/github/google-research/google-research/blob/master/kip/KIP.ipynb" 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>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2011.00071" title="Abstract" id="2011.00071"> arXiv:2011.00071 </a> [<a href="/pdf/2011.00071" title="Download PDF" id="pdf-2011.00071" aria-labelledby="pdf-2011.00071">pdf</a>, <a href="/format/2011.00071" title="Other formats" id="oth-2011.00071" aria-labelledby="oth-2011.00071">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wongpanich,+A">Arissa Wongpanich</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pham,+H">Hieu Pham</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Demmel,+J">James Demmel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tan,+M">Mingxing Tan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Le,+Q">Quoc Le</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=You,+Y">Yang You</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kumar,+S">Sameer Kumar</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); Distributed, Parallel, and Cluster Computing (cs.DC) </div> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2011.00073" title="Abstract" id="2011.00073"> arXiv:2011.00073 </a> [<a href="/pdf/2011.00073" title="Download PDF" id="pdf-2011.00073" aria-labelledby="pdf-2011.00073">pdf</a>, <a href="/format/2011.00073" title="Other formats" id="oth-2011.00073" aria-labelledby="oth-2011.00073">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Resource-Aware Pareto-Optimal Automated Machine Learning Platform </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+Y">Yao Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nam,+A">Andrew Nam</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nasr-Azadani,+M+M">Mohamad M. Nasr-Azadani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tung,+T">Teresa Tung</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted for International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), IEEE. December 2020 </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='item4'>[4]</a> <a href ="/abs/2011.00094" title="Abstract" id="2011.00094"> arXiv:2011.00094 </a> [<a href="/pdf/2011.00094" title="Download PDF" id="pdf-2011.00094" aria-labelledby="pdf-2011.00094">pdf</a>, <a href="/format/2011.00094" title="Other formats" id="oth-2011.00094" aria-labelledby="oth-2011.00094">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Y">Yuan Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zeng,+D">Donglin Zeng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+T">Tianchen Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yuanjia Wang</a></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='item5'>[5]</a> <a href ="/abs/2011.00109" title="Abstract" id="2011.00109"> arXiv:2011.00109 </a> [<a href="/pdf/2011.00109" title="Download PDF" id="pdf-2011.00109" aria-labelledby="pdf-2011.00109">pdf</a>, <a href="/format/2011.00109" title="Other formats" id="oth-2011.00109" aria-labelledby="oth-2011.00109">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Turki,+H">Houcemeddine Turki</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Taieb,+M+A+H">Mohamed Ali Hadj Taieb</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Aouicha,+M+B">Mohamed Ben Aouicha</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Camera-Ready for International Workshop on Data meets Applied Ontologies in Explainable AI (DAO-XAI 2021) </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='item6'>[6]</a> <a href ="/abs/2011.00144" title="Abstract" id="2011.00144"> arXiv:2011.00144 </a> [<a href="/pdf/2011.00144" title="Download PDF" id="pdf-2011.00144" aria-labelledby="pdf-2011.00144">pdf</a>, <a href="/format/2011.00144" title="Other formats" id="oth-2011.00144" aria-labelledby="oth-2011.00144">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Integer Programming-based Error-Correcting Output Code Design for Robust Classification </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gupta,+S">Samarth Gupta</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Amin,+S">Saurabh Amin</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); Information Theory (cs.IT); Computation (stat.CO); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2011.00164" title="Abstract" id="2011.00164"> arXiv:2011.00164 </a> [<a href="/pdf/2011.00164" title="Download PDF" id="pdf-2011.00164" aria-labelledby="pdf-2011.00164">pdf</a>, <a href="/format/2011.00164" title="Other formats" id="oth-2011.00164" aria-labelledby="oth-2011.00164">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Differentially Private ADMM Algorithms for Machine Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+T">Tao Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shang,+F">Fanhua Shang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y">Yuanyuan Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+H">Hongying Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shen,+L">Longjie Shen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gong,+M">Maoguo Gong</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 2 figures </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); Data Structures and Algorithms (cs.DS); Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2011.00177" title="Abstract" id="2011.00177"> arXiv:2011.00177 </a> [<a href="/pdf/2011.00177" title="Download PDF" id="pdf-2011.00177" aria-labelledby="pdf-2011.00177">pdf</a>, <a href="/format/2011.00177" title="Other formats" id="oth-2011.00177" aria-labelledby="oth-2011.00177">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Evaluation of Inference Attack Models for Deep Learning on Medical Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+M">Maoqiang Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+X">Xinyue Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ding,+J">Jiahao Ding</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nguyen,+H">Hien Nguyen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+R">Rong Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pan,+M">Miao Pan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wong,+S+T">Stephen T. Wong</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); Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2011.00179" title="Abstract" id="2011.00179"> arXiv:2011.00179 </a> [<a href="/pdf/2011.00179" title="Download PDF" id="pdf-2011.00179" aria-labelledby="pdf-2011.00179">pdf</a>, <a href="/format/2011.00179" title="Other formats" id="oth-2011.00179" aria-labelledby="oth-2011.00179">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Peng,+S">Shuman Peng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Song,+W">Weilian Song</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ester,+M">Martin Ester</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Code coming soon at <a href="https://github.com/shumanpng/CosML" 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); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2011.00187" title="Abstract" id="2011.00187"> arXiv:2011.00187 </a> [<a href="/pdf/2011.00187" title="Download PDF" id="pdf-2011.00187" aria-labelledby="pdf-2011.00187">pdf</a>, <a href="/format/2011.00187" title="Other formats" id="oth-2011.00187" aria-labelledby="oth-2011.00187">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Novel Semi-Supervised Data-Driven Method for Chiller Fault Diagnosis with Unlabeled Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+B">Bingxu Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cheng,+F">Fanyong Cheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+X">Xin Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cui,+C">Can Cui</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cai,+W">Wenjian Cai</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Applied Energy, Volume 285, 2021, 116459 </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='item11'>[11]</a> <a href ="/abs/2011.00194" title="Abstract" id="2011.00194"> arXiv:2011.00194 </a> [<a href="/pdf/2011.00194" title="Download PDF" id="pdf-2011.00194" aria-labelledby="pdf-2011.00194">pdf</a>, <a href="/format/2011.00194" title="Other formats" id="oth-2011.00194" aria-labelledby="oth-2011.00194">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Rezaabad,+A+L">Ali Lotfi Rezaabad</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kalantari,+R">Rahi Kalantari</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vishwanath,+S">Sriram Vishwanath</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+M">Mingyuan Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tamir,+J">Jonathan Tamir</a></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='item12'>[12]</a> <a href ="/abs/2011.00209" title="Abstract" id="2011.00209"> arXiv:2011.00209 </a> [<a href="/pdf/2011.00209" title="Download PDF" id="pdf-2011.00209" aria-labelledby="pdf-2011.00209">pdf</a>, <a href="/format/2011.00209" title="Other formats" id="oth-2011.00209" aria-labelledby="oth-2011.00209">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Meta-Learning with Adaptive Hyperparameters </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Baik,+S">Sungyong Baik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Choi,+M">Myungsub Choi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Choi,+J">Janghoon Choi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+H">Heewon Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+K+M">Kyoung Mu Lee</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> NeurIPS 2020. Code at <a href="https://github.com/baiksung/alfa" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. Typo fix in the updated version </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='item13'>[13]</a> <a href ="/abs/2011.00213" title="Abstract" id="2011.00213"> arXiv:2011.00213 </a> [<a href="/pdf/2011.00213" title="Download PDF" id="pdf-2011.00213" aria-labelledby="pdf-2011.00213">pdf</a>, <a href="/format/2011.00213" title="Other formats" id="oth-2011.00213" aria-labelledby="oth-2011.00213">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+W">Wenhao Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+X">Xiang Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xie,+G">Guangzeng Xie</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Z">Zhihua Zhang</a></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='item14'>[14]</a> <a href ="/abs/2011.00228" title="Abstract" id="2011.00228"> arXiv:2011.00228 </a> [<a href="/pdf/2011.00228" title="Download PDF" id="pdf-2011.00228" aria-labelledby="pdf-2011.00228">pdf</a>, <a href="/format/2011.00228" title="Other formats" id="oth-2011.00228" aria-labelledby="oth-2011.00228">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal 1-NN Prototypes for Pathological Geometries </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sucholutsky,+I">Ilia Sucholutsky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schonlau,+M">Matthias Schonlau</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages </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='item15'>[15]</a> <a href ="/abs/2011.00241" title="Abstract" id="2011.00241"> arXiv:2011.00241 </a> [<a href="/pdf/2011.00241" title="Download PDF" id="pdf-2011.00241" aria-labelledby="pdf-2011.00241">pdf</a>, <a href="/format/2011.00241" title="Other formats" id="oth-2011.00241" aria-labelledby="oth-2011.00241">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Methods for Pruning Deep Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Vadera,+S">Sunil Vadera</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ameen,+S">Salem Ameen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Major revision that includes additional references and a new section for comparison of results </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neural and Evolutionary Computing (cs.NE) </div> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2011.00288" title="Abstract" id="2011.00288"> arXiv:2011.00288 </a> [<a href="/pdf/2011.00288" title="Download PDF" id="pdf-2011.00288" aria-labelledby="pdf-2011.00288">pdf</a>, <a href="/format/2011.00288" title="Other formats" id="oth-2011.00288" aria-labelledby="oth-2011.00288">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal Sample Complexity of Subgradient Descent for Amplitude Flow via Non-Lipschitz Matrix Concentration </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hand,+P">Paul Hand</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Leong,+O">Oscar Leong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Voroninski,+V">Vladislav Voroninski</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To appear in Communications in Mathematical Sciences </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Information Theory (cs.IT); Optimization and Control (math.OC); Probability (math.PR) </div> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2011.00330" title="Abstract" id="2011.00330"> arXiv:2011.00330 </a> [<a href="/pdf/2011.00330" title="Download PDF" id="pdf-2011.00330" aria-labelledby="pdf-2011.00330">pdf</a>, <a href="/format/2011.00330" title="Other formats" id="oth-2011.00330" aria-labelledby="oth-2011.00330">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Thananjeyan,+B">Brijen Thananjeyan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kandasamy,+K">Kirthevasan Kandasamy</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stoica,+I">Ion Stoica</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jordan,+M+I">Michael I. Jordan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Goldberg,+K">Ken Goldberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gonzalez,+J+E">Joseph E. Gonzalez</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to ICML 2021 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2011.00355" title="Abstract" id="2011.00355"> arXiv:2011.00355 </a> [<a href="/pdf/2011.00355" title="Download PDF" id="pdf-2011.00355" aria-labelledby="pdf-2011.00355">pdf</a>, <a href="/format/2011.00355" title="Other formats" id="oth-2011.00355" aria-labelledby="oth-2011.00355">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Linear Classifiers that Encourage Constructive Adaptation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Y">Yatong Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J">Jialu Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y">Yang Liu</a></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='item19'>[19]</a> <a href ="/abs/2011.00368" title="Abstract" id="2011.00368"> arXiv:2011.00368 </a> [<a href="/pdf/2011.00368" title="Download PDF" id="pdf-2011.00368" aria-labelledby="pdf-2011.00368">pdf</a>, <a href="/format/2011.00368" title="Other formats" id="oth-2011.00368" aria-labelledby="oth-2011.00368">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> DL-Reg: A Deep Learning Regularization Technique using Linear Regression </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Dialameh,+M">Maryam Dialameh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hamzeh,+A">Ali Hamzeh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rahmani,+H">Hossein Rahmani</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2011.00379" title="Abstract" id="2011.00379"> arXiv:2011.00379 </a> [<a href="/pdf/2011.00379" title="Download PDF" id="pdf-2011.00379" aria-labelledby="pdf-2011.00379">pdf</a>, <a href="/format/2011.00379" title="Other formats" id="oth-2011.00379" aria-labelledby="oth-2011.00379">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fair Classification with Group-Dependent Label Noise </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J">Jialu Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y">Yang Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Levy,+C">Caleb Levy</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 9 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) </div> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2011.00382" title="Abstract" id="2011.00382"> arXiv:2011.00382 </a> [<a href="/pdf/2011.00382" title="Download PDF" id="pdf-2011.00382" aria-labelledby="pdf-2011.00382">pdf</a>, <a href="/format/2011.00382" title="Other formats" id="oth-2011.00382" aria-labelledby="oth-2011.00382">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+D">Dong-Ki Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+M">Miao Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Riemer,+M">Matthew Riemer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+C">Chuangchuang Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abdulhai,+M">Marwa Abdulhai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Habibi,+G">Golnaz Habibi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lopez-Cot,+S">Sebastian Lopez-Cot</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tesauro,+G">Gerald Tesauro</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=How,+J+P">Jonathan P. How</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to ICML 2021. Code at <a href="https://github.com/dkkim93/meta-mapg" rel="external noopener nofollow" class="link-external link-https">this https URL</a> and Videos at <a href="https://sites.google.com/view/meta-mapg/home" 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); Multiagent Systems (cs.MA) </div> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2011.00384" title="Abstract" id="2011.00384"> arXiv:2011.00384 </a> [<a href="/pdf/2011.00384" title="Download PDF" id="pdf-2011.00384" aria-labelledby="pdf-2011.00384">pdf</a>, <a href="/format/2011.00384" title="Other formats" id="oth-2011.00384" aria-labelledby="oth-2011.00384">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+M">Meiyi Ma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stankovic,+J">John Stankovic</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bartocci,+E">Ezio Bartocci</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Feng,+L">Lu Feng</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This article appears as part of the ESWEEK-TECS special issue and was presented in the International Conference on Embedded Software (EMSOFT), 2021 </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> In 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS) (pp. 51-62). IEEE </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='item23'>[23]</a> <a href ="/abs/2011.00392" title="Abstract" id="2011.00392"> arXiv:2011.00392 </a> [<a href="/pdf/2011.00392" title="Download PDF" id="pdf-2011.00392" aria-labelledby="pdf-2011.00392">pdf</a>, <a href="/format/2011.00392" title="Other formats" id="oth-2011.00392" aria-labelledby="oth-2011.00392">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Measure Theoretic Approach to Nonuniform Learnability </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bandyopadhyay,+A">Ankit Bandyopadhyay</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitting to STOC 2021 </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='item24'>[24]</a> <a href ="/abs/2011.00401" title="Abstract" id="2011.00401"> arXiv:2011.00401 </a> [<a href="/pdf/2011.00401" title="Download PDF" id="pdf-2011.00401" aria-labelledby="pdf-2011.00401">pdf</a>, <a href="/format/2011.00401" title="Other formats" id="oth-2011.00401" aria-labelledby="oth-2011.00401">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The MAGICAL Benchmark for Robust Imitation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Toyer,+S">Sam Toyer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shah,+R">Rohin Shah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Critch,+A">Andrew Critch</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Russell,+S">Stuart Russell</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> NeurIPS 2020 conference paper (poster) </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='item25'>[25]</a> <a href ="/abs/2011.00424" title="Abstract" id="2011.00424"> arXiv:2011.00424 </a> [<a href="/pdf/2011.00424" title="Download PDF" id="pdf-2011.00424" aria-labelledby="pdf-2011.00424">pdf</a>, <a href="/format/2011.00424" title="Other formats" id="oth-2011.00424" aria-labelledby="oth-2011.00424">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Meisheri,+H">Hardik Meisheri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khadilkar,+H">Harshad Khadilkar</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Multiagent Systems (cs.MA) </div> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2011.00444" title="Abstract" id="2011.00444"> arXiv:2011.00444 </a> [<a href="/pdf/2011.00444" title="Download PDF" id="pdf-2011.00444" aria-labelledby="pdf-2011.00444">pdf</a>, <a href="/format/2011.00444" title="Other formats" id="oth-2011.00444" aria-labelledby="oth-2011.00444">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+K">Keyu Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhuang,+D">Di Zhuang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chang,+J+M">J. Morris Chang</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Neurocomputing Volume 467, 7 January 2022, Pages 418-426 </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/2011.00467" title="Abstract" id="2011.00467"> arXiv:2011.00467 </a> [<a href="/pdf/2011.00467" title="Download PDF" id="pdf-2011.00467" aria-labelledby="pdf-2011.00467">pdf</a>, <a href="/format/2011.00467" title="Other formats" id="oth-2011.00467" aria-labelledby="oth-2011.00467">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Differentially Private Bayesian Inference for Generalized Linear Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kulkarni,+T">Tejas Kulkarni</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=J%C3%A4lk%C3%B6,+J">Joonas J盲lk枚</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Koskela,+A">Antti Koskela</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kaski,+S">Samuel Kaski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Honkela,+A">Antti Honkela</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); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2011.00517" title="Abstract" id="2011.00517"> arXiv:2011.00517 </a> [<a href="/pdf/2011.00517" title="Download PDF" id="pdf-2011.00517" aria-labelledby="pdf-2011.00517">pdf</a>, <a href="/format/2011.00517" title="Other formats" id="oth-2011.00517" aria-labelledby="oth-2011.00517">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+V">Valerie Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gupta,+A">Abhinav Gupta</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Marino,+K">Kenneth Marino</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at ICLR 2021 </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='item29'>[29]</a> <a href ="/abs/2011.00521" title="Abstract" id="2011.00521"> arXiv:2011.00521 </a> [<a href="/pdf/2011.00521" title="Download PDF" id="pdf-2011.00521" aria-labelledby="pdf-2011.00521">pdf</a>, <a href="/format/2011.00521" title="Other formats" id="oth-2011.00521" aria-labelledby="oth-2011.00521">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Neural Network Design: Learning from Neural Architecture Search </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=van+Stein,+B">Bas van Stein</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+H">Hao Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=B%C3%A4ck,+T">Thomas B盲ck</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Neural and Evolutionary Computing (cs.NE) </div> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/2011.00573" title="Abstract" id="2011.00573"> arXiv:2011.00573 </a> [<a href="/pdf/2011.00573" title="Download PDF" id="pdf-2011.00573" aria-labelledby="pdf-2011.00573">pdf</a>, <a href="/format/2011.00573" title="Other formats" id="oth-2011.00573" aria-labelledby="oth-2011.00573">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Two-Level K-FAC Preconditioning for Deep Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Tselepidis,+N">Nikolaos Tselepidis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kohler,+J">Jonas Kohler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Orvieto,+A">Antonio Orvieto</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='item31'>[31]</a> <a href ="/abs/2011.00576" title="Abstract" id="2011.00576"> arXiv:2011.00576 </a> [<a href="/pdf/2011.00576" title="Download PDF" id="pdf-2011.00576" aria-labelledby="pdf-2011.00576">pdf</a>, <a href="/format/2011.00576" title="Other formats" id="oth-2011.00576" aria-labelledby="oth-2011.00576">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Experimental Design for Regret Minimization in Linear Bandits </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wagenmaker,+A">Andrew Wagenmaker</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Katz-Samuels,+J">Julian Katz-Samuels</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jamieson,+K">Kevin Jamieson</a></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='item32'>[32]</a> <a href ="/abs/2011.00580" title="Abstract" id="2011.00580"> arXiv:2011.00580 </a> [<a href="/pdf/2011.00580" title="Download PDF" id="pdf-2011.00580" aria-labelledby="pdf-2011.00580">pdf</a>, <a href="/format/2011.00580" title="Other formats" id="oth-2011.00580" aria-labelledby="oth-2011.00580">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sparsity-Control Ternary Weight Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Deng,+X">Xiang Deng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Z">Zhongfei Zhang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> version 1 of SCA; accepted by journal "Neural Networks"; the final version could be a little different from this version </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='item33'>[33]</a> <a href ="/abs/2011.00603" title="Abstract" id="2011.00603"> arXiv:2011.00603 </a> [<a href="/pdf/2011.00603" title="Download PDF" id="pdf-2011.00603" aria-labelledby="pdf-2011.00603">pdf</a>, <a href="/format/2011.00603" title="Other formats" id="oth-2011.00603" aria-labelledby="oth-2011.00603">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Making ML models fairer through explanations: the case of LimeOut </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Alves,+G">Guilherme Alves</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bhargava,+V">Vaishnavi Bhargava</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Couceiro,+M">Miguel Couceiro</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Napoli,+A">Amedeo Napoli</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 5 figures, 7 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) </div> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2011.00613" title="Abstract" id="2011.00613"> arXiv:2011.00613 </a> [<a href="/pdf/2011.00613" title="Download PDF" id="pdf-2011.00613" aria-labelledby="pdf-2011.00613">pdf</a>, <a href="/format/2011.00613" title="Other formats" id="oth-2011.00613" aria-labelledby="oth-2011.00613">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> An Information-Geometric Distance on the Space of Tasks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+Y">Yansong Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chaudhari,+P">Pratik Chaudhari</a></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/2011.00617" title="Abstract" id="2011.00617"> arXiv:2011.00617 </a> [<a href="/pdf/2011.00617" title="Download PDF" id="pdf-2011.00617" aria-labelledby="pdf-2011.00617">pdf</a>, <a href="/format/2011.00617" title="Other formats" id="oth-2011.00617" aria-labelledby="oth-2011.00617">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Support vector machines and Radon's theorem </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Adams,+H">Henry Adams</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Farnell,+E">Elin Farnell</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Story,+B">Brittany Story</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Combinatorics (math.CO); General Topology (math.GN); Statistics Theory (math.ST) </div> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2011.00639" title="Abstract" id="2011.00639"> arXiv:2011.00639 </a> [<a href="/pdf/2011.00639" title="Download PDF" id="pdf-2011.00639" aria-labelledby="pdf-2011.00639">pdf</a>, <a href="/format/2011.00639" title="Other formats" id="oth-2011.00639" aria-labelledby="oth-2011.00639">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Model-Agnostic Explanations using Minimal Forcing Subsets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+X">Xing Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ghosh,+J">Joydeep Ghosh</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> International Joint Conference on Neural Networks (IJCNN), 2021 </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='item37'>[37]</a> <a href ="/abs/2011.00697" title="Abstract" id="2011.00697"> arXiv:2011.00697 </a> [<a href="/pdf/2011.00697" title="Download PDF" id="pdf-2011.00697" aria-labelledby="pdf-2011.00697">pdf</a>, <a href="/format/2011.00697" title="Other formats" id="oth-2011.00697" aria-labelledby="oth-2011.00697">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Time Series Forecasting with Stacked Long Short-Term Memory Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xiao,+F">Frank Xiao</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 7 pages, 8 figures </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='item38'>[38]</a> <a href ="/abs/2011.00702" title="Abstract" id="2011.00702"> arXiv:2011.00702 </a> [<a href="/pdf/2011.00702" title="Download PDF" id="pdf-2011.00702" aria-labelledby="pdf-2011.00702">pdf</a>, <a href="/format/2011.00702" title="Other formats" id="oth-2011.00702" aria-labelledby="oth-2011.00702">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fast Reinforcement Learning with Incremental Gaussian Mixture Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Pinto,+R">Rafael Pinto</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 17 pages, 8 figures </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='item39'>[39]</a> <a href ="/abs/2011.00716" title="Abstract" id="2011.00716"> arXiv:2011.00716 </a> [<a href="/pdf/2011.00716" title="Download PDF" id="pdf-2011.00716" aria-labelledby="pdf-2011.00716">pdf</a>, <a href="/format/2011.00716" title="Other formats" id="oth-2011.00716" aria-labelledby="oth-2011.00716">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> PAC Confidence Predictions for Deep Neural Network Classifiers </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Park,+S">Sangdon Park</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+S">Shuo Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+I">Insup Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bastani,+O">Osbert Bastani</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to ICLR 2021 </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='item40'>[40]</a> <a href ="/abs/2011.00717" title="Abstract" id="2011.00717"> arXiv:2011.00717 </a> [<a href="/pdf/2011.00717" title="Download PDF" id="pdf-2011.00717" aria-labelledby="pdf-2011.00717">pdf</a>, <a href="/format/2011.00717" title="Other formats" id="oth-2011.00717" aria-labelledby="oth-2011.00717">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Noise-Contrastive Estimation for Multivariate Point Processes </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mei,+H">Hongyuan Mei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wan,+T">Tom Wan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Eisner,+J">Jason Eisner</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> NeurIPS 2020 camera-ready </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='item41'>[41]</a> <a href ="/abs/2011.00745" title="Abstract" id="2011.00745"> arXiv:2011.00745 </a> [<a href="/pdf/2011.00745" title="Download PDF" id="pdf-2011.00745" aria-labelledby="pdf-2011.00745">pdf</a>, <a href="/format/2011.00745" title="Other formats" id="oth-2011.00745" aria-labelledby="oth-2011.00745">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Transport based Graph Kernels </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+K">Kai Ma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wan,+P">Peng Wan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+D">Daoqiang Zhang</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/2011.00748" title="Abstract" id="2011.00748"> arXiv:2011.00748 </a> [<a href="/pdf/2011.00748" title="Download PDF" id="pdf-2011.00748" aria-labelledby="pdf-2011.00748">pdf</a>, <a href="/format/2011.00748" title="Other formats" id="oth-2011.00748" aria-labelledby="oth-2011.00748">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Interpreting Graph Drawing with Multi-Agent Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Safarli,+I">Ilkin Safarli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+Y">Youjia Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+B">Bei Wang</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='item43'>[43]</a> <a href ="/abs/2011.00753" title="Abstract" id="2011.00753"> arXiv:2011.00753 </a> [<a href="/pdf/2011.00753" title="Download PDF" id="pdf-2011.00753" aria-labelledby="pdf-2011.00753">pdf</a>, <a href="/format/2011.00753" title="Other formats" id="oth-2011.00753" aria-labelledby="oth-2011.00753">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Das,+S+S+S">Sarkar Snigdha Sarathi Das</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shanto,+S+K">Subangkar Karmaker Shanto</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rahman,+M">Masum Rahman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Islam,+M+S">Md. Saiful Islam</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rahman,+A">Atif Rahman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Masud,+M+M">Mohammad Mehedy Masud</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ali,+M+E">Mohammed Eunus Ali</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> IMWUT March 2022, Vol 6 Article 8 (UbiComp 2022) </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 1, Article 8 (March 2022), 21 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Signal Processing (eess.SP) </div> </div> </dd> <dt> <a name='item44'>[44]</a> <a href ="/abs/2011.00754" title="Abstract" id="2011.00754"> arXiv:2011.00754 </a> [<a href="/pdf/2011.00754" title="Download PDF" id="pdf-2011.00754" aria-labelledby="pdf-2011.00754">pdf</a>, <a href="/format/2011.00754" title="Other formats" id="oth-2011.00754" aria-labelledby="oth-2011.00754">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Toward a Generalization Metric for Deep Generative Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Thanh-Tung,+H">Hoang Thanh-Tung</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tran,+T">Truyen Tran</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 1st I Can't Believe It's Not Better Workshop (ICBINB@NeurIPS 2020). Source code is available at <a href="https://github.com/htt210/GeneralizationMetricGAN" 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> </div> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2011.00771" title="Abstract" id="2011.00771"> arXiv:2011.00771 </a> [<a href="/pdf/2011.00771" title="Download PDF" id="pdf-2011.00771" aria-labelledby="pdf-2011.00771">pdf</a>, <a href="/format/2011.00771" title="Other formats" id="oth-2011.00771" aria-labelledby="oth-2011.00771">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multitask Learning and Joint Optimization for Transformer-RNN-Transducer Speech Recognition </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Jeon,+J">Jae-Jin Jeon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+E">Eesung Kim</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Sound (cs.SD); Audio and Speech Processing (eess.AS) </div> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2011.00791" title="Abstract" id="2011.00791"> arXiv:2011.00791 </a> [<a href="/pdf/2011.00791" title="Download PDF" id="pdf-2011.00791" aria-labelledby="pdf-2011.00791">pdf</a>, <a href="/format/2011.00791" title="Other formats" id="oth-2011.00791" aria-labelledby="oth-2011.00791">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Cooperative Heterogeneous Deep Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zheng,+H">Han Zheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wei,+P">Pengfei Wei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+J">Jing Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Long,+G">Guodong Long</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lu,+Q">Qinghua Lu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+C">Chengqi Zhang</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='item47'>[47]</a> <a href ="/abs/2011.00792" title="Abstract" id="2011.00792"> arXiv:2011.00792 </a> [<a href="/pdf/2011.00792" title="Download PDF" id="pdf-2011.00792" aria-labelledby="pdf-2011.00792">pdf</a>, <a href="/format/2011.00792" title="Other formats" id="oth-2011.00792" aria-labelledby="oth-2011.00792">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Flexible Class of Dependence-aware Multi-Label Loss Functions </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=H%C3%BCllermeier,+E">Eyke H眉llermeier</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wever,+M">Marcel Wever</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mencia,+E+L">Eneldo Loza Mencia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=F%C3%BCrnkranz,+J">Johannes F眉rnkranz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rapp,+M">Michael Rapp</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='item48'>[48]</a> <a href ="/abs/2011.00810" title="Abstract" id="2011.00810"> arXiv:2011.00810 </a> [<a href="/pdf/2011.00810" title="Download PDF" id="pdf-2011.00810" aria-labelledby="pdf-2011.00810">pdf</a>, <a href="/format/2011.00810" title="Other formats" id="oth-2011.00810" aria-labelledby="oth-2011.00810">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Aggregating Incomplete and Noisy Rankings </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Fotakis,+D">Dimitris Fotakis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kalavasis,+A">Alkis Kalavasis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stavropoulos,+K">Konstantinos Stavropoulos</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 21 pages, 3 figures. Minor changes and experimental results added in this version. Corresponding to the camera-ready version that appeared in the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021) </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2278-2286, 2021 </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='item49'>[49]</a> <a href ="/abs/2011.00813" title="Abstract" id="2011.00813"> arXiv:2011.00813 </a> [<a href="/pdf/2011.00813" title="Download PDF" id="pdf-2011.00813" aria-labelledby="pdf-2011.00813">pdf</a>, <a href="/format/2011.00813" title="Other formats" id="oth-2011.00813" aria-labelledby="oth-2011.00813">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multi-Armed Bandits with Censored Consumption of Resources </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bengs,+V">Viktor Bengs</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=H%C3%BCllermeier,+E">Eyke H眉llermeier</a></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='item50'>[50]</a> <a href ="/abs/2011.00819" title="Abstract" id="2011.00819"> arXiv:2011.00819 </a> [<a href="/pdf/2011.00819" title="Download PDF" id="pdf-2011.00819" aria-labelledby="pdf-2011.00819">pdf</a>, <a href="/format/2011.00819" title="Other formats" id="oth-2011.00819" aria-labelledby="oth-2011.00819">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Self-Concordant Analysis of Generalized Linear Bandits with Forgetting </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Russac,+Y">Yoan Russac</a> (DI-ENS, CNRS, PSL, VALDA), <a href="https://arxiv.org/search/cs?searchtype=author&query=Faury,+L">Louis Faury</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Capp%C3%A9,+O">Olivier Capp茅</a> (DI-ENS, VALDA), <a href="https://arxiv.org/search/cs?searchtype=author&query=Garivier,+A">Aur茅lien Garivier</a> (UMPA-ENSL)</div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> AISTATS 2021 - International Conference on Artificial Intelligence and Statistics, Apr 2021, San Diego / Virtual, United States </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> </dl> <div class='paging'>Total of 2185 entries : <span>1-50</span> <a href=/list/cs.LG/2020-11?skip=50&show=50>51-100</a> <a href=/list/cs.LG/2020-11?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2020-11?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2020-11?skip=2150&show=50>2151-2185</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2020-11?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2020-11?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2020-11?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; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; 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