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Machine Learning Oct 2022
<!DOCTYPE html> <html lang="en"> <head> <title>Machine Learning Oct 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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class='paging'>Total of 3231 entries : <span>1-50</span> <a href=/list/cs.LG/2022-10?skip=50&show=50>51-100</a> <a href=/list/cs.LG/2022-10?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2022-10?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2022-10?skip=3200&show=50>3201-3231</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2022-10?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2022-10?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2022-10?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/2210.00025" title="Abstract" id="2210.00025"> arXiv:2210.00025 </a> [<a href="/pdf/2210.00025" title="Download PDF" id="pdf-2210.00025" aria-labelledby="pdf-2210.00025">pdf</a>, <a href="/format/2210.00025" title="Other formats" id="oth-2210.00025" aria-labelledby="oth-2210.00025">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Banerjee,+S">Siddhartha Banerjee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sinclair,+S+R">Sean R. Sinclair</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tambe,+M">Milind Tambe</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+L">Lily Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+C+L">Christina Lee Yu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 55 pages (30 pages main paper), 9 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='item2'>[2]</a> <a href ="/abs/2210.00032" title="Abstract" id="2210.00032"> arXiv:2210.00032 </a> [<a href="/pdf/2210.00032" title="Download PDF" id="pdf-2210.00032" aria-labelledby="pdf-2210.00032">pdf</a>, <a href="/format/2210.00032" title="Other formats" id="oth-2210.00032" aria-labelledby="oth-2210.00032">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chanpuriya,+S">Sudhanshu Chanpuriya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rossi,+R+A">Ryan A. Rossi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+S">Sungchul Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+T">Tong Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hoffswell,+J">Jane Hoffswell</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lipka,+N">Nedim Lipka</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guo,+S">Shunan Guo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Musco,+C">Cameron Musco</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Social and Information Networks (cs.SI) </div> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2210.00035" title="Abstract" id="2210.00035"> arXiv:2210.00035 </a> [<a href="/pdf/2210.00035" title="Download PDF" id="pdf-2210.00035" aria-labelledby="pdf-2210.00035">pdf</a>, <a href="/format/2210.00035" title="Other formats" id="oth-2210.00035" aria-labelledby="oth-2210.00035">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Neural Causal Models for Counterfactual Identification and Estimation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xia,+K">Kevin Xia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pan,+Y">Yushu Pan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bareinboim,+E">Elias Bareinboim</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 10 pages main body, 57 pages total, 23 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='item4'>[4]</a> <a href ="/abs/2210.00036" title="Abstract" id="2210.00036"> arXiv:2210.00036 </a> [<a href="/pdf/2210.00036" title="Download PDF" id="pdf-2210.00036" aria-labelledby="pdf-2210.00036">pdf</a>, <a href="https://arxiv.org/html/2210.00036v3" title="View HTML" id="html-2210.00036" aria-labelledby="html-2210.00036" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2210.00036" title="Other formats" id="oth-2210.00036" aria-labelledby="oth-2210.00036">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Differentially Private Bias-Term Fine-tuning of Foundation Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bu,+Z">Zhiqi Bu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yu-Xiang Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zha,+S">Sheng Zha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Karypis,+G">George Karypis</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at ICML 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computation and Language (cs.CL); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2210.00038" title="Abstract" id="2210.00038"> arXiv:2210.00038 </a> [<a href="/pdf/2210.00038" title="Download PDF" id="pdf-2210.00038" aria-labelledby="pdf-2210.00038">pdf</a>, <a href="/format/2210.00038" title="Other formats" id="oth-2210.00038" aria-labelledby="oth-2210.00038">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Differentially Private Optimization on Large Model at Small Cost </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bu,+Z">Zhiqi Bu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yu-Xiang Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zha,+S">Sheng Zha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Karypis,+G">George Karypis</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computation and Language (cs.CL); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2210.00044" title="Abstract" id="2210.00044"> arXiv:2210.00044 </a> [<a href="/pdf/2210.00044" title="Download PDF" id="pdf-2210.00044" aria-labelledby="pdf-2210.00044">pdf</a>, <a href="https://arxiv.org/html/2210.00044v2" title="View HTML" id="html-2210.00044" aria-labelledby="html-2210.00044" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2210.00044" title="Other formats" id="oth-2210.00044" aria-labelledby="oth-2210.00044">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nikandrou,+M">Mavina Nikandrou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+L">Lu Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Suglia,+A">Alessandro Suglia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Konstas,+I">Ioannis Konstas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rieser,+V">Verena Rieser</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='item7'>[7]</a> <a href ="/abs/2210.00053" title="Abstract" id="2210.00053"> arXiv:2210.00053 </a> [<a href="/pdf/2210.00053" title="Download PDF" id="pdf-2210.00053" aria-labelledby="pdf-2210.00053">pdf</a>, <a href="/format/2210.00053" title="Other formats" id="oth-2210.00053" aria-labelledby="oth-2210.00053">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nasirigerdeh,+R">Reza Nasirigerdeh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Torkzadehmahani,+J">Javad Torkzadehmahani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rueckert,+D">Daniel Rueckert</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kaissis,+G">Georgios Kaissis</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To appear in the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), February 2023 </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> 1st IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023 </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='item8'>[8]</a> <a href ="/abs/2210.00055" title="Abstract" id="2210.00055"> arXiv:2210.00055 </a> [<a href="/pdf/2210.00055" title="Download PDF" id="pdf-2210.00055" aria-labelledby="pdf-2210.00055">pdf</a>, <a href="/format/2210.00055" title="Other formats" id="oth-2210.00055" aria-labelledby="oth-2210.00055">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MaskTune: Mitigating Spurious Correlations by Forcing to Explore </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Taghanaki,+S+A">Saeid Asgari Taghanaki</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khani,+A">Aliasghar Khani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khani,+F">Fereshte Khani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gholami,+A">Ali Gholami</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tran,+L">Linh Tran</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mahdavi-Amiri,+A">Ali Mahdavi-Amiri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hamarneh,+G">Ghassan Hamarneh</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to NeurIPS 2022 </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='item9'>[9]</a> <a href ="/abs/2210.00060" title="Abstract" id="2210.00060"> arXiv:2210.00060 </a> [<a href="/pdf/2210.00060" title="Download PDF" id="pdf-2210.00060" aria-labelledby="pdf-2210.00060">pdf</a>, <a href="/format/2210.00060" title="Other formats" id="oth-2210.00060" aria-labelledby="oth-2210.00060">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> FedTrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated in Smart Grids </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Al-Quraan,+M">Mohammad Al-Quraan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khan,+A">Ahsan Khan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Centeno,+A">Anthony Centeno</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zoha,+A">Ahmed Zoha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Imran,+M+A">Muhammad Ali Imran</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mohjazi,+L">Lina Mohjazi</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='item10'>[10]</a> <a href ="/abs/2210.00062" title="Abstract" id="2210.00062"> arXiv:2210.00062 </a> [<a href="/pdf/2210.00062" title="Download PDF" id="pdf-2210.00062" aria-labelledby="pdf-2210.00062">pdf</a>, <a href="/format/2210.00062" title="Other formats" id="oth-2210.00062" aria-labelledby="oth-2210.00062">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Robust Kernel Ensembles with Kernel Average Pooling </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bashivan,+P">Pouya Bashivan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ibrahim,+A">Adam Ibrahim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dehghani,+A">Amirozhan Dehghani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ren,+Y">Yifei Ren</a></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='item11'>[11]</a> <a href ="/abs/2210.00064" title="Abstract" id="2210.00064"> arXiv:2210.00064 </a> [<a href="/pdf/2210.00064" title="Download PDF" id="pdf-2210.00064" aria-labelledby="pdf-2210.00064">pdf</a>, <a href="/format/2210.00064" title="Other formats" id="oth-2210.00064" aria-labelledby="oth-2210.00064">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> CEREAL: Few-Sample Clustering Evaluation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nayak,+N+V">Nihal V. Nayak</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Elenberg,+E+R">Ethan R. Elenberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rosenbaum,+C">Clemens Rosenbaum</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='item12'>[12]</a> <a href ="/abs/2210.00065" title="Abstract" id="2210.00065"> arXiv:2210.00065 </a> [<a href="/pdf/2210.00065" title="Download PDF" id="pdf-2210.00065" aria-labelledby="pdf-2210.00065">pdf</a>, <a href="/format/2210.00065" title="Other formats" id="oth-2210.00065" aria-labelledby="oth-2210.00065">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Application of Deep Q Learning with Simulation Results for Elevator Optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cao,+Z">Zheng Cao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guo,+R">Raymond Guo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tuguinay,+C+M">Caesar M. Tuguinay</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pock,+M">Mark Pock</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+J">Jiayi Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Ziyu Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 16 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2210.00066" title="Abstract" id="2210.00066"> arXiv:2210.00066 </a> [<a href="/pdf/2210.00066" title="Download PDF" id="pdf-2210.00066" aria-labelledby="pdf-2210.00066">pdf</a>, <a href="/format/2210.00066" title="Other formats" id="oth-2210.00066" aria-labelledby="oth-2210.00066">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Improving Policy Learning via Language Dynamics Distillation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhong,+V">Victor Zhong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mu,+J">Jesse Mu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zettlemoyer,+L">Luke Zettlemoyer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Grefenstette,+E">Edward Grefenstette</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rockt%C3%A4schel,+T">Tim Rockt盲schel</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to NeurIPS 2022. 16 pages, 12 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) </div> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2210.00068" title="Abstract" id="2210.00068"> arXiv:2210.00068 </a> [<a href="/pdf/2210.00068" title="Download PDF" id="pdf-2210.00068" aria-labelledby="pdf-2210.00068">pdf</a>, <a href="/format/2210.00068" title="Other formats" id="oth-2210.00068" aria-labelledby="oth-2210.00068">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multi-Task Option Learning and Discovery for Stochastic Path Planning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Shah,+N">Naman Shah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Siddharth">Siddharth Srivastava</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Robotics (cs.RO) </div> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2210.00069" title="Abstract" id="2210.00069"> arXiv:2210.00069 </a> [<a href="/pdf/2210.00069" title="Download PDF" id="pdf-2210.00069" aria-labelledby="pdf-2210.00069">pdf</a>, <a href="/format/2210.00069" title="Other formats" id="oth-2210.00069" aria-labelledby="oth-2210.00069">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Topological Singularity Detection at Multiple Scales </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=von+Rohrscheidt,+J">Julius von Rohrscheidt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rieck,+B">Bastian Rieck</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at the International Conference on Machine Learning (ICML) 2023; camera-ready version </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Algebraic Topology (math.AT); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2210.00074" title="Abstract" id="2210.00074"> arXiv:2210.00074 </a> [<a href="/pdf/2210.00074" title="Download PDF" id="pdf-2210.00074" aria-labelledby="pdf-2210.00074">pdf</a>, <a href="/format/2210.00074" title="Other formats" id="oth-2210.00074" aria-labelledby="oth-2210.00074">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Rahman,+M">M. Rahman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khan,+A">Abid Khan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Anowar,+S">Sayeed Anowar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Al-Imran,+M">Md Al-Imran</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Verma,+R">Richa Verma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kumar,+D">Dinesh Kumar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kobayashi,+K">Kazuma Kobayashi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alam,+S">Syed Alam</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Applications (stat.AP) </div> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2210.00084" title="Abstract" id="2210.00084"> arXiv:2210.00084 </a> [<a href="/pdf/2210.00084" title="Download PDF" id="pdf-2210.00084" aria-labelledby="pdf-2210.00084">pdf</a>, <a href="/format/2210.00084" title="Other formats" id="oth-2210.00084" aria-labelledby="oth-2210.00084">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Contrastive Graph Few-Shot Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+C">Chunhui Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+H">Hongfu Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+J">Jundong Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ye,+Y">Yanfang Ye</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+C">Chuxu 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='item18'>[18]</a> <a href ="/abs/2210.00089" title="Abstract" id="2210.00089"> arXiv:2210.00089 </a> [<a href="/pdf/2210.00089" title="Download PDF" id="pdf-2210.00089" aria-labelledby="pdf-2210.00089">pdf</a>, <a href="/format/2210.00089" title="Other formats" id="oth-2210.00089" aria-labelledby="oth-2210.00089">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Papatheodoulou,+D">Dimitris Papatheodoulou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pavlou,+P">Pavlos Pavlou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vrachimis,+S+G">Stelios G. Vrachimis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Malialis,+K">Kleanthis Malialis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Eliades,+D+G">Demetrios G. Eliades</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Theocharides,+T">Theocharis Theocharides</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 18th International Conference on Artificial Intelligence Applications and Innovations (AIAI), 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='item19'>[19]</a> <a href ="/abs/2210.00090" title="Abstract" id="2210.00090"> arXiv:2210.00090 </a> [<a href="/pdf/2210.00090" title="Download PDF" id="pdf-2210.00090" aria-labelledby="pdf-2210.00090">pdf</a>, <a href="/format/2210.00090" title="Other formats" id="oth-2210.00090" aria-labelledby="oth-2210.00090">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=So,+O">Oswin So</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+G">Gongjie Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Theodorou,+E+A">Evangelos A. Theodorou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tao,+M">Molei Tao</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='item20'>[20]</a> <a href ="/abs/2210.00092" title="Abstract" id="2210.00092"> arXiv:2210.00092 </a> [<a href="/pdf/2210.00092" title="Download PDF" id="pdf-2210.00092" aria-labelledby="pdf-2210.00092">pdf</a>, <a href="/format/2210.00092" title="Other formats" id="oth-2210.00092" aria-labelledby="oth-2210.00092">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Federated Training of Dual Encoding Models on Small Non-IID Client Datasets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Vemulapalli,+R">Raviteja Vemulapalli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Morningstar,+W+R">Warren Richard Morningstar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mansfield,+P+A">Philip Andrew Mansfield</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Eichner,+H">Hubert Eichner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Singhal,+K">Karan Singhal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Afkanpour,+A">Arash Afkanpour</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Green,+B">Bradley Green</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICLR 2023 Workshop on Pitfalls of Limited Data and Computation for Trustworthy ML </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='item21'>[21]</a> <a href ="/abs/2210.00094" title="Abstract" id="2210.00094"> arXiv:2210.00094 </a> [<a href="/pdf/2210.00094" title="Download PDF" id="pdf-2210.00094" aria-labelledby="pdf-2210.00094">pdf</a>, <a href="https://arxiv.org/html/2210.00094v2" title="View HTML" id="html-2210.00094" aria-labelledby="html-2210.00094" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2210.00094" title="Other formats" id="oth-2210.00094" aria-labelledby="oth-2210.00094">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Improving Robustness with Adaptive Weight Decay </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ghiasi,+A">Amin Ghiasi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shafahi,+A">Ali Shafahi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ardekani,+R">Reza Ardekani</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='item22'>[22]</a> <a href ="/abs/2210.00102" title="Abstract" id="2210.00102"> arXiv:2210.00102 </a> [<a href="/pdf/2210.00102" title="Download PDF" id="pdf-2210.00102" aria-labelledby="pdf-2210.00102">pdf</a>, <a href="/format/2210.00102" title="Other formats" id="oth-2210.00102" aria-labelledby="oth-2210.00102">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+X">Xiaotian Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+T">Tong Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Y">Yozen Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+X">Xia Hu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shah,+N">Neil Shah</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted by ICLR2023 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Social and Information Networks (cs.SI) </div> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2210.00107" title="Abstract" id="2210.00107"> arXiv:2210.00107 </a> [<a href="/pdf/2210.00107" title="Download PDF" id="pdf-2210.00107" aria-labelledby="pdf-2210.00107">pdf</a>, <a href="/format/2210.00107" title="Other formats" id="oth-2210.00107" aria-labelledby="oth-2210.00107">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Contrastive Corpus Attribution for Explaining Representations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+C">Chris Lin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+H">Hugh Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+C">Chanwoo Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+S">Su-In Lee</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Updated for the final camera-ready version of ICLR 2023 </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='item24'>[24]</a> <a href ="/abs/2210.00108" title="Abstract" id="2210.00108"> arXiv:2210.00108 </a> [<a href="/pdf/2210.00108" title="Download PDF" id="pdf-2210.00108" aria-labelledby="pdf-2210.00108">pdf</a>, <a href="https://arxiv.org/html/2210.00108v4" title="View HTML" id="html-2210.00108" aria-labelledby="html-2210.00108" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2210.00108" title="Other formats" id="oth-2210.00108" aria-labelledby="oth-2210.00108">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Clifford,+E">Eleanor Clifford</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shumailov,+I">Ilia Shumailov</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+Y">Yiren Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Anderson,+R">Ross Anderson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mullins,+R">Robert Mullins</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 10 pages, 7 figures, to be published in IEEE Secure and Trustworthy Machine Learning 2024. For website see <a href="https://ml.backdoors.uk" rel="external noopener nofollow" class="link-external link-https">this https URL</a> . For source code, see <a href="https://sr.ht/~ecc/ImpNet" 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>; Cryptography and Security (cs.CR) </div> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2210.00116" title="Abstract" id="2210.00116"> arXiv:2210.00116 </a> [<a href="/pdf/2210.00116" title="Download PDF" id="pdf-2210.00116" aria-labelledby="pdf-2210.00116">pdf</a>, <a href="https://arxiv.org/html/2210.00116v3" title="View HTML" id="html-2210.00116" aria-labelledby="html-2210.00116" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2210.00116" title="Other formats" id="oth-2210.00116" aria-labelledby="oth-2210.00116">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+Y">Yulun Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Barton,+R+A">Robert A. Barton</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Zichen Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ioannidis,+V+N">Vassilis N. Ioannidis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=De+Donno,+C">Carlo De Donno</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Price,+L+C">Layne C. Price</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Voloch,+L+F">Luis F. Voloch</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Karypis,+G">George Karypis</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Genomics (q-bio.GN); Methodology (stat.ME); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2210.00122" title="Abstract" id="2210.00122"> arXiv:2210.00122 </a> [<a href="/pdf/2210.00122" title="Download PDF" id="pdf-2210.00122" aria-labelledby="pdf-2210.00122">pdf</a>, <a href="/format/2210.00122" title="Other formats" id="oth-2210.00122" aria-labelledby="oth-2210.00122">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Adversarial Robustness of Representation Learning for Knowledge Graphs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bhardwaj,+P">Peru Bhardwaj</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> PhD Thesis at Trinity College Dublin, Ireland </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) </div> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2210.00124" title="Abstract" id="2210.00124"> arXiv:2210.00124 </a> [<a href="/pdf/2210.00124" title="Download PDF" id="pdf-2210.00124" aria-labelledby="pdf-2210.00124">pdf</a>, <a href="/format/2210.00124" title="Other formats" id="oth-2210.00124" aria-labelledby="oth-2210.00124">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Implicit Neural Spatial Representations for Time-dependent PDEs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+H">Honglin Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+R">Rundi Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Grinspun,+E">Eitan Grinspun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zheng,+C">Changxi Zheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+P+Y">Peter Yichen Chen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICML 2023. Project page: <a href="http://www.cs.columbia.edu/cg/INSR-PDE/" rel="external noopener nofollow" class="link-external link-http">this http URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computational Engineering, Finance, and Science (cs.CE); Graphics (cs.GR); Numerical Analysis (math.NA) </div> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2210.00136" title="Abstract" id="2210.00136"> arXiv:2210.00136 </a> [<a href="/pdf/2210.00136" title="Download PDF" id="pdf-2210.00136" aria-labelledby="pdf-2210.00136">pdf</a>, <a href="/format/2210.00136" title="Other formats" id="oth-2210.00136" aria-labelledby="oth-2210.00136">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> IMB-NAS: Neural Architecture Search for Imbalanced Datasets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Duggal,+R">Rahul Duggal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Peng,+S">Shengyun Peng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+H">Hao Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chau,+D+H">Duen Horng Chau</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='item29'>[29]</a> <a href ="/abs/2210.00162" title="Abstract" id="2210.00162"> arXiv:2210.00162 </a> [<a href="/pdf/2210.00162" title="Download PDF" id="pdf-2210.00162" aria-labelledby="pdf-2210.00162">pdf</a>, <a href="/format/2210.00162" title="Other formats" id="oth-2210.00162" aria-labelledby="oth-2210.00162">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+C">Chunhui Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+C">Chao Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tian,+Y">Yijun Tian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wen,+Q">Qianlong Wen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ouyang,+Z">Zhongyu Ouyang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Y">Youhuan Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ye,+Y">Yanfang Ye</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+C">Chuxu 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='item30'>[30]</a> <a href ="/abs/2210.00173" title="Abstract" id="2210.00173"> arXiv:2210.00173 </a> [<a href="/pdf/2210.00173" title="Download PDF" id="pdf-2210.00173" aria-labelledby="pdf-2210.00173">pdf</a>, <a href="/format/2210.00173" title="Other formats" id="oth-2210.00173" aria-labelledby="oth-2210.00173">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Predictive Inference with Feature Conformal Prediction </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Teng,+J">Jiaye Teng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wen,+C">Chuan Wen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+D">Dinghuai Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bengio,+Y">Yoshua Bengio</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+Y">Yang Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yuan,+Y">Yang Yuan</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Published as a conference paper at ICLR 2023 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Methodology (stat.ME); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/2210.00176" title="Abstract" id="2210.00176"> arXiv:2210.00176 </a> [<a href="/pdf/2210.00176" title="Download PDF" id="pdf-2210.00176" aria-labelledby="pdf-2210.00176">pdf</a>, <a href="/format/2210.00176" title="Other formats" id="oth-2210.00176" aria-labelledby="oth-2210.00176">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Combinatorial Perspective on the Optimization of Shallow ReLU Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Matena,+M">Michael Matena</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Raffel,+C">Colin Raffel</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/2210.00178" title="Abstract" id="2210.00178"> arXiv:2210.00178 </a> [<a href="/pdf/2210.00178" title="Download PDF" id="pdf-2210.00178" aria-labelledby="pdf-2210.00178">pdf</a>, <a href="/format/2210.00178" title="Other formats" id="oth-2210.00178" aria-labelledby="oth-2210.00178">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On the tightness of linear relaxation based robustness certification methods </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Tang,+C">Cheng Tang</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='item33'>[33]</a> <a href ="/abs/2210.00189" title="Abstract" id="2210.00189"> arXiv:2210.00189 </a> [<a href="/pdf/2210.00189" title="Download PDF" id="pdf-2210.00189" aria-labelledby="pdf-2210.00189">pdf</a>, <a href="/format/2210.00189" title="Other formats" id="oth-2210.00189" aria-labelledby="oth-2210.00189">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Pitfalls of Gaussians as a noise distribution in NCE </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+H">Holden Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pabbaraju,+C">Chirag Pabbaraju</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sevekari,+A">Anish Sevekari</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Risteski,+A">Andrej Risteski</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 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> <dt> <a name='item34'>[34]</a> <a href ="/abs/2210.00211" title="Abstract" id="2210.00211"> arXiv:2210.00211 </a> [<a href="/pdf/2210.00211" title="Download PDF" id="pdf-2210.00211" aria-labelledby="pdf-2210.00211">pdf</a>, <a href="/format/2210.00211" title="Other formats" id="oth-2210.00211" aria-labelledby="oth-2210.00211">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Boosting Exploration in Actor-Critic Algorithms by Incentivizing Plausible Novel States </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Banerjee,+C">Chayan Banerjee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Z">Zhiyong Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Noman,+N">Nasimul Noman</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='item35'>[35]</a> <a href ="/abs/2210.00213" title="Abstract" id="2210.00213"> arXiv:2210.00213 </a> [<a href="/pdf/2210.00213" title="Download PDF" id="pdf-2210.00213" aria-labelledby="pdf-2210.00213">pdf</a>, <a href="/format/2210.00213" title="Other formats" id="oth-2210.00213" aria-labelledby="oth-2210.00213">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> HyperHawkes: Hypernetwork based Neural Temporal Point Process </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Dubey,+M">Manisha Dubey</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=P.K.">P.K. Srijith</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Desarkar,+M+S">Maunendra Sankar Desarkar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 9 pages, 2 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='item36'>[36]</a> <a href ="/abs/2210.00222" title="Abstract" id="2210.00222"> arXiv:2210.00222 </a> [<a href="/pdf/2210.00222" title="Download PDF" id="pdf-2210.00222" aria-labelledby="pdf-2210.00222">pdf</a>, <a href="/format/2210.00222" title="Other formats" id="oth-2210.00222" aria-labelledby="oth-2210.00222">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Solving Coupled Differential Equation Groups Using PINO-CDE </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ding,+W">Wenhao Ding</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=He,+Q">Qing He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tong,+H">Hanghang Tong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Q">Qingjing Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+P">Ping 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='item37'>[37]</a> <a href ="/abs/2210.00226" title="Abstract" id="2210.00226"> arXiv:2210.00226 </a> [<a href="/pdf/2210.00226" title="Download PDF" id="pdf-2210.00226" aria-labelledby="pdf-2210.00226">pdf</a>, <a href="https://arxiv.org/html/2210.00226v5" title="View HTML" id="html-2210.00226" aria-labelledby="html-2210.00226" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2210.00226" title="Other formats" id="oth-2210.00226" aria-labelledby="oth-2210.00226">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Shi,+Y">Yujun Shi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liang,+J">Jian Liang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+W">Wenqing Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tan,+V+Y+F">Vincent Y. F. Tan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bai,+S">Song Bai</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> camera ready version of ICLR 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='item38'>[38]</a> <a href ="/abs/2210.00244" title="Abstract" id="2210.00244"> arXiv:2210.00244 </a> [<a href="/pdf/2210.00244" title="Download PDF" id="pdf-2210.00244" aria-labelledby="pdf-2210.00244">pdf</a>, <a href="/format/2210.00244" title="Other formats" id="oth-2210.00244" aria-labelledby="oth-2210.00244">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On The Relative Error of Random Fourier Features for Preserving Kernel Distance </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cheng,+K">Kuan Cheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+S+H">Shaofeng H.-C. Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wei,+L">Luojian Wei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wei,+Z">Zhide Wei</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Data Structures and Algorithms (cs.DS) </div> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/2210.00248" title="Abstract" id="2210.00248"> arXiv:2210.00248 </a> [<a href="/pdf/2210.00248" title="Download PDF" id="pdf-2210.00248" aria-labelledby="pdf-2210.00248">pdf</a>, <a href="/format/2210.00248" title="Other formats" id="oth-2210.00248" aria-labelledby="oth-2210.00248">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Heterogeneous Graph Contrastive Multi-view Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Zehong Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Q">Qi Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+D">Donghua Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+X">Xiaolong Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+X">Xiao-Zhi Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shen,+S">Shigen Shen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted by SDM 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='item40'>[40]</a> <a href ="/abs/2210.00269" title="Abstract" id="2210.00269"> arXiv:2210.00269 </a> [<a href="/pdf/2210.00269" title="Download PDF" id="pdf-2210.00269" aria-labelledby="pdf-2210.00269">pdf</a>, <a href="/format/2210.00269" title="Other formats" id="oth-2210.00269" aria-labelledby="oth-2210.00269">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Solar Power Time Series Forecasting Utilising Wavelet Coefficients </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Almaghrabi,+S">Sarah Almaghrabi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rana,+M">Mashud Rana</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hamilton,+M">Margaret Hamilton</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rahaman,+M+S">Mohammad Saiedur Rahaman</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Neurocomputing Neurocomputing Volume 508, 7 October 2022, Pages 182-207 </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='item41'>[41]</a> <a href ="/abs/2210.00272" title="Abstract" id="2210.00272"> arXiv:2210.00272 </a> [<a href="/pdf/2210.00272" title="Download PDF" id="pdf-2210.00272" aria-labelledby="pdf-2210.00272">pdf</a>, <a href="/format/2210.00272" title="Other formats" id="oth-2210.00272" aria-labelledby="oth-2210.00272">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Matsubara,+T">Takashi Matsubara</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yaguchi,+T">Takaharu Yaguchi</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 25 pages </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> The Eleventh International Conference on Learning Representations (ICLR2023) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Dynamical Systems (math.DS); Numerical Analysis (math.NA) </div> </div> </dd> <dt> <a name='item42'>[42]</a> <a href ="/abs/2210.00292" title="Abstract" id="2210.00292"> arXiv:2210.00292 </a> [<a href="/pdf/2210.00292" title="Download PDF" id="pdf-2210.00292" aria-labelledby="pdf-2210.00292">pdf</a>, <a href="/format/2210.00292" title="Other formats" id="oth-2210.00292" aria-labelledby="oth-2210.00292">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> DeltaBound Attack: Efficient decision-based attack in low queries regime </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Rossi,+L">Lorenzo Rossi</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) </div> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2210.00293" title="Abstract" id="2210.00293"> arXiv:2210.00293 </a> [<a href="/pdf/2210.00293" title="Download PDF" id="pdf-2210.00293" aria-labelledby="pdf-2210.00293">pdf</a>, <a href="/format/2210.00293" title="Other formats" id="oth-2210.00293" aria-labelledby="oth-2210.00293">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Deep Intrinsically Motivated Exploration in Continuous Control </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Saglam,+B">Baturay Saglam</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kozat,+S+S">Suleyman S. Kozat</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='item44'>[44]</a> <a href ="/abs/2210.00294" title="Abstract" id="2210.00294"> arXiv:2210.00294 </a> [<a href="/pdf/2210.00294" title="Download PDF" id="pdf-2210.00294" aria-labelledby="pdf-2210.00294">pdf</a>, <a href="/format/2210.00294" title="Other formats" id="oth-2210.00294" aria-labelledby="oth-2210.00294">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Gait-based Age Group Classification with Adaptive Graph Neural Network </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Aderinola,+T+B">Timilehin B. Aderinola</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Connie,+T">Tee Connie</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ong,+T+S">Thian Song Ong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Teoh,+A+B+J">Andrew Beng Jin Teoh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Goh,+M+K+O">Michael Kah Ong Goh</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Engineering Applications of Artificial Intelligence, 137, 109081 (2024) </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='item45'>[45]</a> <a href ="/abs/2210.00299" title="Abstract" id="2210.00299"> arXiv:2210.00299 </a> [<a href="/pdf/2210.00299" title="Download PDF" id="pdf-2210.00299" aria-labelledby="pdf-2210.00299">pdf</a>, <a href="/format/2210.00299" title="Other formats" id="oth-2210.00299" aria-labelledby="oth-2210.00299">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Federated Representation Learning via Maximal Coding Rate Reduction </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cervino,+J">Juan Cervino</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=NaderiAlizadeh,+N">Navid NaderiAlizadeh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ribeiro,+A">Alejandro Ribeiro</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Image and Video Processing (eess.IV); Systems and Control (eess.SY) </div> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2210.00301" title="Abstract" id="2210.00301"> arXiv:2210.00301 </a> [<a href="/pdf/2210.00301" title="Download PDF" id="pdf-2210.00301" aria-labelledby="pdf-2210.00301">pdf</a>, <a href="/format/2210.00301" title="Other formats" id="oth-2210.00301" aria-labelledby="oth-2210.00301">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Globally Smooth Functions on Manifolds </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cervino,+J">Juan Cervino</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chamon,+L+F+O">Luiz F. O. Chamon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Haeffele,+B+D">Benjamin D. Haeffele</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vidal,+R">Rene Vidal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ribeiro,+A">Alejandro Ribeiro</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='item47'>[47]</a> <a href ="/abs/2210.00310" title="Abstract" id="2210.00310"> arXiv:2210.00310 </a> [<a href="/pdf/2210.00310" title="Download PDF" id="pdf-2210.00310" aria-labelledby="pdf-2210.00310">pdf</a>, <a href="/format/2210.00310" title="Other formats" id="oth-2210.00310" aria-labelledby="oth-2210.00310">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Clustering for directed graphs using parametrized random walk diffusion kernels </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sevi,+H">Harry Sevi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jonckheere,+M">Matthieu Jonckheere</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kalogeratos,+A">Argyris Kalogeratos</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='item48'>[48]</a> <a href ="/abs/2210.00317" title="Abstract" id="2210.00317"> arXiv:2210.00317 </a> [<a href="/pdf/2210.00317" title="Download PDF" id="pdf-2210.00317" aria-labelledby="pdf-2210.00317">pdf</a>, <a href="/format/2210.00317" title="Other formats" id="oth-2210.00317" aria-labelledby="oth-2210.00317">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Implementation of a Three-class Classification LS-SVM Model for the Hybrid Antenna Array with Bowtie Elements in the Adaptive Beamforming Application </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Komeylian,+S">Somayeh Komeylian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Paolini,+C">Christopher Paolini</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/2210.00319" title="Abstract" id="2210.00319"> arXiv:2210.00319 </a> [<a href="/pdf/2210.00319" title="Download PDF" id="pdf-2210.00319" aria-labelledby="pdf-2210.00319">pdf</a>, <a href="/format/2210.00319" title="Other formats" id="oth-2210.00319" aria-labelledby="oth-2210.00319">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> PathFinder: Discovering Decision Pathways in Deep Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=%C4%B0rsoy,+O">Ozan 陌rsoy</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alpayd%C4%B1n,+E">Ethem Alpayd谋n</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='item50'>[50]</a> <a href ="/abs/2210.00340" title="Abstract" id="2210.00340"> arXiv:2210.00340 </a> [<a href="/pdf/2210.00340" title="Download PDF" id="pdf-2210.00340" aria-labelledby="pdf-2210.00340">pdf</a>, <a href="https://arxiv.org/html/2210.00340v3" title="View HTML" id="html-2210.00340" aria-labelledby="html-2210.00340" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2210.00340" title="Other formats" id="oth-2210.00340" aria-labelledby="oth-2210.00340">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bayati,+M">Mohsen Bayati</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cao,+J">Junyu Cao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+W">Wanning Chen</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> </dl> <div class='paging'>Total of 3231 entries : <span>1-50</span> <a href=/list/cs.LG/2022-10?skip=50&show=50>51-100</a> <a href=/list/cs.LG/2022-10?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2022-10?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2022-10?skip=3200&show=50>3201-3231</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2022-10?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2022-10?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2022-10?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"> <!-- Macro-Column 1 --> <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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