Machine Learning Mar 2024

<!DOCTYPE html> <html lang="en"> <head> <title>Machine Learning Mar 2024</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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href=/list/cs.LG/2024-03?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2024-03?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2024-03?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/2403.00011" title="Abstract" id="2403.00011"> arXiv:2403.00011 </a> [<a href="/pdf/2403.00011" title="Download PDF" id="pdf-2403.00011" aria-labelledby="pdf-2403.00011">pdf</a>, <a href="https://arxiv.org/html/2403.00011v1" title="View HTML" id="html-2403.00011" aria-labelledby="html-2403.00011" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00011" title="Other formats" id="oth-2403.00011" aria-labelledby="oth-2403.00011">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Introducing User Feedback-based Counterfactual Explanations (UFCE) </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Suffian,+M">Muhammad Suffian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alonso-Moral,+J+M">Jose M. Alonso-Moral</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bogliolo,+A">Alessandro Bogliolo</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> preprint of paper submitted to IJCIS Springer </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) </div> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2403.00012" title="Abstract" id="2403.00012"> arXiv:2403.00012 </a> [<a href="/pdf/2403.00012" title="Download PDF" id="pdf-2403.00012" aria-labelledby="pdf-2403.00012">pdf</a>, <a href="https://arxiv.org/html/2403.00012v2" title="View HTML" id="html-2403.00012" aria-labelledby="html-2403.00012" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00012" title="Other formats" id="oth-2403.00012" aria-labelledby="oth-2403.00012">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhong,+R">Ruizhe Zhong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ye,+J">Junjie Ye</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tang,+Z">Zhentao Tang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kai,+S">Shixiong Kai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yuan,+M">Mingxuan Yuan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hao,+J">Jianye Hao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yan,+J">Junchi Yan</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 13 pages, 5 figures, The 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Hardware Architecture (cs.AR) </div> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2403.00013" title="Abstract" id="2403.00013"> arXiv:2403.00013 </a> [<a href="/pdf/2403.00013" title="Download PDF" id="pdf-2403.00013" aria-labelledby="pdf-2403.00013">pdf</a>, <a href="https://arxiv.org/html/2403.00013v2" title="View HTML" id="html-2403.00013" aria-labelledby="html-2403.00013" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00013" title="Other formats" id="oth-2403.00013" aria-labelledby="oth-2403.00013">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Prioritizing Informative Features and Examples for Deep Learning from Noisy Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Park,+D">Dongmin Park</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> PhD thesis </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/2403.00016" title="Abstract" id="2403.00016"> arXiv:2403.00016 </a> [<a href="/pdf/2403.00016" title="Download PDF" id="pdf-2403.00016" aria-labelledby="pdf-2403.00016">pdf</a>, <a href="https://arxiv.org/html/2403.00016v1" title="View HTML" id="html-2403.00016" aria-labelledby="html-2403.00016" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00016" title="Other formats" id="oth-2403.00016" aria-labelledby="oth-2403.00016">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Deep Sensitivity Analysis for Objective-Oriented Combinatorial Optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gireesan,+G">Ganga Gireesan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pillai,+N">Nisha Pillai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rothrock,+M+J">Michael J Rothrock</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nanduri,+B">Bindu Nanduri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Z">Zhiqian Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ramkumar,+M">Mahalingam Ramkumar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> The 2023 International Conference on Computational Science & Computational Intelligence (CSCI'23) </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='item5'>[5]</a> <a href ="/abs/2403.00017" title="Abstract" id="2403.00017"> arXiv:2403.00017 </a> [<a href="/pdf/2403.00017" title="Download PDF" id="pdf-2403.00017" aria-labelledby="pdf-2403.00017">pdf</a>, <a href="https://arxiv.org/html/2403.00017v1" title="View HTML" id="html-2403.00017" aria-labelledby="html-2403.00017" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00017" title="Other formats" id="oth-2403.00017" aria-labelledby="oth-2403.00017">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards Interpreting Multi-Objective Feature Associations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Pillai,+N">Nisha Pillai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gireesan,+G">Ganga Gireesan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rothrock,+M+J">Michael J. Rothrock Jr.</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nanduri,+B">Bindu Nanduri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Z">Zhiqian Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ramkumar,+M">Mahalingam Ramkumar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> The 18th Annual IEEE International Systems Conference 2024 (IEEE SYSCON 2024) </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='item6'>[6]</a> <a href ="/abs/2403.00019" title="Abstract" id="2403.00019"> arXiv:2403.00019 </a> [<a href="/pdf/2403.00019" title="Download PDF" id="pdf-2403.00019" aria-labelledby="pdf-2403.00019">pdf</a>, <a href="/format/2403.00019" title="Other formats" id="oth-2403.00019" aria-labelledby="oth-2403.00019">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Transformer-based Parameter Estimation in Statistics </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yin,+X">Xiaoxin Yin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yin,+D+S">David S. Yin</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='item7'>[7]</a> <a href ="/abs/2403.00024" title="Abstract" id="2403.00024"> arXiv:2403.00024 </a> [<a href="/pdf/2403.00024" title="Download PDF" id="pdf-2403.00024" aria-labelledby="pdf-2403.00024">pdf</a>, <a href="https://arxiv.org/html/2403.00024v2" title="View HTML" id="html-2403.00024" aria-labelledby="html-2403.00024" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00024" title="Other formats" id="oth-2403.00024" aria-labelledby="oth-2403.00024">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bini,+L">Lorenzo Bini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mojarrad,+F+N">Fatemeh Nassajian Mojarrad</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liarou,+M">Margarita Liarou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Matthes,+T">Thomas Matthes</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Marchand-Maillet,+S">St茅phane Marchand-Maillet</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> arXiv admin note: text overlap with <a href="https://arxiv.org/abs/2402.18611" data-arxiv-id="2402.18611" class="link-https">arXiv:2402.18611</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Quantitative Methods (q-bio.QM) </div> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2403.00025" title="Abstract" id="2403.00025"> arXiv:2403.00025 </a> [<a href="/pdf/2403.00025" title="Download PDF" id="pdf-2403.00025" aria-labelledby="pdf-2403.00025">pdf</a>, <a href="https://arxiv.org/html/2403.00025v3" title="View HTML" id="html-2403.00025" aria-labelledby="html-2403.00025" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00025" title="Other formats" id="oth-2403.00025" aria-labelledby="oth-2403.00025">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On the Challenges and Opportunities in Generative AI </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Manduchi,+L">Laura Manduchi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pandey,+K">Kushagra Pandey</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Meister,+C">Clara Meister</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bamler,+R">Robert Bamler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cotterell,+R">Ryan Cotterell</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=D%C3%A4ubener,+S">Sina D盲ubener</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fellenz,+S">Sophie Fellenz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fischer,+A">Asja Fischer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=G%C3%A4rtner,+T">Thomas G盲rtner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kirchler,+M">Matthias Kirchler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kloft,+M">Marius Kloft</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Y">Yingzhen Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lippert,+C">Christoph Lippert</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=de+Melo,+G">Gerard de Melo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nalisnick,+E">Eric Nalisnick</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ommer,+B">Bj枚rn Ommer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ranganath,+R">Rajesh Ranganath</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rudolph,+M">Maja Rudolph</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ullrich,+K">Karen Ullrich</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Van+den+Broeck,+G">Guy Van den Broeck</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vogt,+J+E">Julia E Vogt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yixin Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wenzel,+F">Florian Wenzel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wood,+F">Frank Wood</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mandt,+S">Stephan Mandt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fortuin,+V">Vincent Fortuin</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='item9'>[9]</a> <a href ="/abs/2403.00026" title="Abstract" id="2403.00026"> arXiv:2403.00026 </a> [<a href="/pdf/2403.00026" title="Download PDF" id="pdf-2403.00026" aria-labelledby="pdf-2403.00026">pdf</a>, <a href="https://arxiv.org/html/2403.00026v1" title="View HTML" id="html-2403.00026" aria-labelledby="html-2403.00026" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00026" title="Other formats" id="oth-2403.00026" aria-labelledby="oth-2403.00026">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chin,+S+J+K">Samuel J. K. Chin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Winkenbach,+M">Matthias Winkenbach</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Akash">Akash 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); Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2403.00036" title="Abstract" id="2403.00036"> arXiv:2403.00036 </a> [<a href="/pdf/2403.00036" title="Download PDF" id="pdf-2403.00036" aria-labelledby="pdf-2403.00036">pdf</a>, <a href="https://arxiv.org/html/2403.00036v1" title="View HTML" id="html-2403.00036" aria-labelledby="html-2403.00036" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00036" title="Other formats" id="oth-2403.00036" aria-labelledby="oth-2403.00036">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Influencing Bandits: Arm Selection for Preference Shaping </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nadkarni,+V">Viraj Nadkarni</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Manjunath,+D">D. Manjunath</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Moharir,+S">Sharayu Moharir</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages, 8 figures, 24 references, proofs in appendix </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Systems and Control (eess.SY) </div> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/2403.00041" title="Abstract" id="2403.00041"> arXiv:2403.00041 </a> [<a href="/pdf/2403.00041" title="Download PDF" id="pdf-2403.00041" aria-labelledby="pdf-2403.00041">pdf</a>, <a href="https://arxiv.org/html/2403.00041v2" title="View HTML" id="html-2403.00041" aria-labelledby="html-2403.00041" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00041" title="Other formats" id="oth-2403.00041" aria-labelledby="oth-2403.00041">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Global and Local Prompts Cooperation via Optimal Transport for Federated Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+H">Hongxia Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+W">Wei Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J">Jingya Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shi,+Y">Ye Shi</a></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) </div> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2403.00103" title="Abstract" id="2403.00103"> arXiv:2403.00103 </a> [<a href="/pdf/2403.00103" title="Download PDF" id="pdf-2403.00103" aria-labelledby="pdf-2403.00103">pdf</a>, <a href="https://arxiv.org/html/2403.00103v1" title="View HTML" id="html-2403.00103" aria-labelledby="html-2403.00103" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00103" title="Other formats" id="oth-2403.00103" aria-labelledby="oth-2403.00103">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Holtz,+C">Chester Holtz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yucheng Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cheng,+C">Chung-Kuan Cheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+B">Bill Lin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 7 pages, 7 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Hardware Architecture (cs.AR) </div> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2403.00105" title="Abstract" id="2403.00105"> arXiv:2403.00105 </a> [<a href="/pdf/2403.00105" title="Download PDF" id="pdf-2403.00105" aria-labelledby="pdf-2403.00105">pdf</a>, <a href="https://arxiv.org/html/2403.00105v1" title="View HTML" id="html-2403.00105" aria-labelledby="html-2403.00105" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00105" title="Other formats" id="oth-2403.00105" aria-labelledby="oth-2403.00105">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Longitudinal Counterfactuals: Constraints and Opportunities </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Asemota,+A">Alexander Asemota</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hooker,+G">Giles Hooker</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computers and Society (cs.CY) </div> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2403.00116" title="Abstract" id="2403.00116"> arXiv:2403.00116 </a> [<a href="/pdf/2403.00116" title="Download PDF" id="pdf-2403.00116" aria-labelledby="pdf-2403.00116">pdf</a>, <a href="https://arxiv.org/html/2403.00116v1" title="View HTML" id="html-2403.00116" aria-labelledby="html-2403.00116" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00116" title="Other formats" id="oth-2403.00116" aria-labelledby="oth-2403.00116">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Federated Linear Contextual Bandits with Heterogeneous Clients </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Blaser,+E">Ethan Blaser</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+C">Chuanhao Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+H">Hongning 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='item15'>[15]</a> <a href ="/abs/2403.00131" title="Abstract" id="2403.00131"> arXiv:2403.00131 </a> [<a href="/pdf/2403.00131" title="Download PDF" id="pdf-2403.00131" aria-labelledby="pdf-2403.00131">pdf</a>, <a href="https://arxiv.org/html/2403.00131v3" title="View HTML" id="html-2403.00131" aria-labelledby="html-2403.00131" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00131" title="Other formats" id="oth-2403.00131" aria-labelledby="oth-2403.00131">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> UniTS: A Unified Multi-Task Time Series Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+S">Shanghua Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Koker,+T">Teddy Koker</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Queen,+O">Owen Queen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hartvigsen,+T">Thomas Hartvigsen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tsiligkaridis,+T">Theodoros Tsiligkaridis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zitnik,+M">Marinka Zitnik</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> NeurIPS 2024 </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='item16'>[16]</a> <a href ="/abs/2403.00155" title="Abstract" id="2403.00155"> arXiv:2403.00155 </a> [<a href="/pdf/2403.00155" title="Download PDF" id="pdf-2403.00155" aria-labelledby="pdf-2403.00155">pdf</a>, <a href="https://arxiv.org/html/2403.00155v1" title="View HTML" id="html-2403.00155" aria-labelledby="html-2403.00155" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00155" title="Other formats" id="oth-2403.00155" aria-labelledby="oth-2403.00155">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards Explaining Deep Neural Network Compression Through a Probabilistic Latent Space </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mozafari-Nia,+M">Mahsa Mozafari-Nia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sekeh,+S+Y">Salimeh Yasaei Sekeh</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='item17'>[17]</a> <a href ="/abs/2403.00157" title="Abstract" id="2403.00157"> arXiv:2403.00157 </a> [<a href="/pdf/2403.00157" title="Download PDF" id="pdf-2403.00157" aria-labelledby="pdf-2403.00157">pdf</a>, <a href="/format/2403.00157" title="Other formats" id="oth-2403.00157" aria-labelledby="oth-2403.00157">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Privacy-Preserving Distributed Optimization and Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Z">Ziqin Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yongqiang Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted as a chapter in the Encyclopedia of Systems and Control Engineering published by Elsevier </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 Science and Game Theory (cs.GT) </div> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2403.00176" title="Abstract" id="2403.00176"> arXiv:2403.00176 </a> [<a href="/pdf/2403.00176" title="Download PDF" id="pdf-2403.00176" aria-labelledby="pdf-2403.00176">pdf</a>, <a href="https://arxiv.org/html/2403.00176v1" title="View HTML" id="html-2403.00176" aria-labelledby="html-2403.00176" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00176" title="Other formats" id="oth-2403.00176" aria-labelledby="oth-2403.00176">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> SoD$^2$: Statically Optimizing Dynamic Deep Neural Network </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Niu,+W">Wei Niu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Agrawal,+G">Gagan Agrawal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ren,+B">Bin 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); Programming Languages (cs.PL) </div> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/2403.00177" title="Abstract" id="2403.00177"> arXiv:2403.00177 </a> [<a href="/pdf/2403.00177" title="Download PDF" id="pdf-2403.00177" aria-labelledby="pdf-2403.00177">pdf</a>, <a href="https://arxiv.org/html/2403.00177v3" title="View HTML" id="html-2403.00177" aria-labelledby="html-2403.00177" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00177" title="Other formats" id="oth-2403.00177" aria-labelledby="oth-2403.00177">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kuang,+K">Keying Kuang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dean,+F">Frances Dean</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jedlicki,+J+B">Jack B. Jedlicki</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ouyang,+D">David Ouyang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Philippakis,+A">Anthony Philippakis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sontag,+D">David Sontag</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alaa,+A+M">Ahmed M. Alaa</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Quantitative Methods (q-bio.QM) </div> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2403.00178" title="Abstract" id="2403.00178"> arXiv:2403.00178 </a> [<a href="/pdf/2403.00178" title="Download PDF" id="pdf-2403.00178" aria-labelledby="pdf-2403.00178">pdf</a>, <a href="https://arxiv.org/html/2403.00178v1" title="View HTML" id="html-2403.00178" aria-labelledby="html-2403.00178" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00178" title="Other formats" id="oth-2403.00178" aria-labelledby="oth-2403.00178">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+Z">Zijie Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hwang,+J">Jeehyun Hwang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+J">Junkai Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Baik,+J">Jinwoo Baik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+W">Weitong Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wodarz,+D">Dominik Wodarz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+Y">Yizhou Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gu,+Q">Quanquan Gu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+W">Wei 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='item21'>[21]</a> <a href ="/abs/2403.00188" title="Abstract" id="2403.00188"> arXiv:2403.00188 </a> [<a href="/pdf/2403.00188" title="Download PDF" id="pdf-2403.00188" aria-labelledby="pdf-2403.00188">pdf</a>, <a href="https://arxiv.org/html/2403.00188v2" title="View HTML" id="html-2403.00188" aria-labelledby="html-2403.00188" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00188" title="Other formats" id="oth-2403.00188" aria-labelledby="oth-2403.00188">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Impact of Decentralized Learning on Player Utilities in Stackelberg Games </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Donahue,+K">Kate Donahue</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Immorlica,+N">Nicole Immorlica</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jagadeesan,+M">Meena Jagadeesan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lucier,+B">Brendan Lucier</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Slivkins,+A">Aleksandrs Slivkins</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To appear at ICML 2024; this is the full version </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computer Science and Game Theory (cs.GT) </div> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2403.00194" title="Abstract" id="2403.00194"> arXiv:2403.00194 </a> [<a href="/pdf/2403.00194" title="Download PDF" id="pdf-2403.00194" aria-labelledby="pdf-2403.00194">pdf</a>, <a href="https://arxiv.org/html/2403.00194v2" title="View HTML" id="html-2403.00194" aria-labelledby="html-2403.00194" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00194" title="Other formats" id="oth-2403.00194" aria-labelledby="oth-2403.00194">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Ask Your Distribution Shift if Pre-Training is Right for You </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cohen-Wang,+B">Benjamin Cohen-Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vendrow,+J">Joshua Vendrow</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Madry,+A">Aleksander Madry</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2403.00222" title="Abstract" id="2403.00222"> arXiv:2403.00222 </a> [<a href="/pdf/2403.00222" title="Download PDF" id="pdf-2403.00222" aria-labelledby="pdf-2403.00222">pdf</a>, <a href="https://arxiv.org/html/2403.00222v3" title="View HTML" id="html-2403.00222" aria-labelledby="html-2403.00222" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00222" title="Other formats" id="oth-2403.00222" aria-labelledby="oth-2403.00222">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Anand,+E">Emile Anand</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qu,+G">Guannan Qu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 34 pages, 6 figures </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='item24'>[24]</a> <a href ="/abs/2403.00225" title="Abstract" id="2403.00225"> arXiv:2403.00225 </a> [<a href="/pdf/2403.00225" title="Download PDF" id="pdf-2403.00225" aria-labelledby="pdf-2403.00225">pdf</a>, <a href="https://arxiv.org/html/2403.00225v3" title="View HTML" id="html-2403.00225" aria-labelledby="html-2403.00225" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00225" title="Other formats" id="oth-2403.00225" aria-labelledby="oth-2403.00225">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Robust Policy Learning via Offline Skill Diffusion </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+W+K">Woo Kyung Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yoo,+M">Minjong Yoo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Woo,+H">Honguk Woo</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 6 figures; Accepted for AAAI Conference on Artificial Intelligence (AAAI 2024); Published version </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='item25'>[25]</a> <a href ="/abs/2403.00254" title="Abstract" id="2403.00254"> arXiv:2403.00254 </a> [<a href="/pdf/2403.00254" title="Download PDF" id="pdf-2403.00254" aria-labelledby="pdf-2403.00254">pdf</a>, <a href="https://arxiv.org/html/2403.00254v1" title="View HTML" id="html-2403.00254" aria-labelledby="html-2403.00254" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00254" title="Other formats" id="oth-2403.00254" aria-labelledby="oth-2403.00254">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Cloud-based Federated Learning Framework for MRI Segmentation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Prajapati,+R">Rukesh Prajapati</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=El-Wakeel,+A+S">Amr S. El-Wakeel</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='item26'>[26]</a> <a href ="/abs/2403.00273" title="Abstract" id="2403.00273"> arXiv:2403.00273 </a> [<a href="/pdf/2403.00273" title="Download PDF" id="pdf-2403.00273" aria-labelledby="pdf-2403.00273">pdf</a>, <a href="https://arxiv.org/html/2403.00273v1" title="View HTML" id="html-2403.00273" aria-labelledby="html-2403.00273" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00273" title="Other formats" id="oth-2403.00273" aria-labelledby="oth-2403.00273">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ARED: Argentina Real Estate Dataset </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Belenky,+I">Iv谩n Belenky</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 3 pages, 6 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Digital Libraries (cs.DL); Statistical Finance (q-fin.ST) </div> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2403.00276" title="Abstract" id="2403.00276"> arXiv:2403.00276 </a> [<a href="/pdf/2403.00276" title="Download PDF" id="pdf-2403.00276" aria-labelledby="pdf-2403.00276">pdf</a>, <a href="/format/2403.00276" title="Other formats" id="oth-2403.00276" aria-labelledby="oth-2403.00276">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Graph Construction with Flexible Nodes for Traffic Demand Prediction </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hou,+J">Jinyan Hou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+S">Shan Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Ya Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qin,+H">Haotong Qin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> We have decided to withdraw this paper temporarily as we have identified areas that require further refinement and additional research. Our goal is to ensure the highest quality and accuracy of our work before it is made available to the broader academic community. We appreciate your understanding and will submit an updated version once these improvements have been completed </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='item28'>[28]</a> <a href ="/abs/2403.00278" title="Abstract" id="2403.00278"> arXiv:2403.00278 </a> [<a href="/pdf/2403.00278" title="Download PDF" id="pdf-2403.00278" aria-labelledby="pdf-2403.00278">pdf</a>, <a href="https://arxiv.org/html/2403.00278v2" title="View HTML" id="html-2403.00278" aria-labelledby="html-2403.00278" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00278" title="Other formats" id="oth-2403.00278" aria-labelledby="oth-2403.00278">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Shifted Interpolation for Differential Privacy </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bok,+J">Jinho Bok</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Su,+W">Weijie Su</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Altschuler,+J+M">Jason M. Altschuler</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 45 pages, ICML 2024. v2: added lower bounds (Appendix C.5) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Cryptography and Security (cs.CR); Optimization and Control (math.OC); Statistics Theory (math.ST); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2403.00282" title="Abstract" id="2403.00282"> arXiv:2403.00282 </a> [<a href="/pdf/2403.00282" title="Download PDF" id="pdf-2403.00282" aria-labelledby="pdf-2403.00282">pdf</a>, <a href="https://arxiv.org/html/2403.00282v2" title="View HTML" id="html-2403.00282" aria-labelledby="html-2403.00282" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00282" title="Other formats" id="oth-2403.00282" aria-labelledby="oth-2403.00282">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+D">Dohyeong Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hong,+M">Mineui Hong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Park,+J">Jeongho Park</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Oh,+S">Songhwai Oh</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 25 pages </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/2403.00337" title="Abstract" id="2403.00337"> arXiv:2403.00337 </a> [<a href="/pdf/2403.00337" title="Download PDF" id="pdf-2403.00337" aria-labelledby="pdf-2403.00337">pdf</a>, <a href="https://arxiv.org/html/2403.00337v1" title="View HTML" id="html-2403.00337" aria-labelledby="html-2403.00337" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00337" title="Other formats" id="oth-2403.00337" aria-labelledby="oth-2403.00337">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Nonlinear Sheaf Diffusion in Graph Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zaghen,+O">Olga Zaghen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Thesis for Master's degree in Artificial Intelligence Systems (University of Trento), 65 pages </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/2403.00403" title="Abstract" id="2403.00403"> arXiv:2403.00403 </a> [<a href="/pdf/2403.00403" title="Download PDF" id="pdf-2403.00403" aria-labelledby="pdf-2403.00403">pdf</a>, <a href="https://arxiv.org/html/2403.00403v1" title="View HTML" id="html-2403.00403" aria-labelledby="html-2403.00403" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00403" title="Other formats" id="oth-2403.00403" aria-labelledby="oth-2403.00403">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fractal interpolation in the context of prediction accuracy optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Baicoianu,+A">Alexandra Baicoianu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gavril%C4%83,+C+G">Cristina Gabriela Gavril膬</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pacurar,+C+M">Cristina Maria Pacurar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pacurar,+V+D">Victor Dan Pacurar</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/2403.00409" title="Abstract" id="2403.00409"> arXiv:2403.00409 </a> [<a href="/pdf/2403.00409" title="Download PDF" id="pdf-2403.00409" aria-labelledby="pdf-2403.00409">pdf</a>, <a href="https://arxiv.org/html/2403.00409v2" title="View HTML" id="html-2403.00409" aria-labelledby="html-2403.00409" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00409" title="Other formats" id="oth-2403.00409" aria-labelledby="oth-2403.00409">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Provably Robust DPO: Aligning Language Models with Noisy Feedback </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chowdhury,+S+R">Sayak Ray Chowdhury</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kini,+A">Anush Kini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Natarajan,+N">Nagarajan Natarajan</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) </div> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2403.00420" title="Abstract" id="2403.00420"> arXiv:2403.00420 </a> [<a href="/pdf/2403.00420" title="Download PDF" id="pdf-2403.00420" aria-labelledby="pdf-2403.00420">pdf</a>, <a href="/format/2403.00420" title="Other formats" id="oth-2403.00420" aria-labelledby="oth-2403.00420">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Schott,+L">Lucas Schott</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Delas,+J">Josephine Delas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hajri,+H">Hatem Hajri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gherbi,+E">Elies Gherbi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yaich,+R">Reda Yaich</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Boulahia-Cuppens,+N">Nora Boulahia-Cuppens</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cuppens,+F">Frederic Cuppens</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lamprier,+S">Sylvain Lamprier</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 61 pages, 17 figues, 1 table </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2403.00485" title="Abstract" id="2403.00485"> arXiv:2403.00485 </a> [<a href="/pdf/2403.00485" title="Download PDF" id="pdf-2403.00485" aria-labelledby="pdf-2403.00485">pdf</a>, <a href="https://arxiv.org/html/2403.00485v2" title="View HTML" id="html-2403.00485" aria-labelledby="html-2403.00485" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00485" title="Other formats" id="oth-2403.00485" aria-labelledby="oth-2403.00485">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+J">Jiaqi Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cen,+J">Jiacheng Cen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+L">Liming Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Z">Zongzhao Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kong,+X">Xiangzhe Kong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiao,+R">Rui Jiao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+Z">Ziyang Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+T">Tingyang Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+F">Fandi Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Zihe Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+H">Hongteng Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wei,+Z">Zhewei Wei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+D">Deli Zhao</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=Rong,+Y">Yu Rong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+W">Wenbing Huang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> The article has been accepted by Frontiers of Computer Science (FCS), with the DOI: {<a href="https://doi.org/10.1007/s11704-025-41426-w" data-doi="10.1007/s11704-025-41426-w" class="link-https link-external" rel="external noopener nofollow">https://doi.org/10.1007/s11704-025-41426-w</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='item35'>[35]</a> <a href ="/abs/2403.00514" title="Abstract" id="2403.00514"> arXiv:2403.00514 </a> [<a href="/pdf/2403.00514" title="Download PDF" id="pdf-2403.00514" aria-labelledby="pdf-2403.00514">pdf</a>, <a href="https://arxiv.org/html/2403.00514v2" title="View HTML" id="html-2403.00514" aria-labelledby="html-2403.00514" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00514" title="Other formats" id="oth-2403.00514" aria-labelledby="oth-2403.00514">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nauman,+M">Michal Nauman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bortkiewicz,+M">Micha艂 Bortkiewicz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mi%C5%82o%C5%9B,+P">Piotr Mi艂o艣</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Trzci%C5%84ski,+T">Tomasz Trzci艅ski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ostaszewski,+M">Mateusz Ostaszewski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cygan,+M">Marek Cygan</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICML 2024 </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/2403.00540" title="Abstract" id="2403.00540"> arXiv:2403.00540 </a> [<a href="/pdf/2403.00540" title="Download PDF" id="pdf-2403.00540" aria-labelledby="pdf-2403.00540">pdf</a>, <a href="https://arxiv.org/html/2403.00540v3" title="View HTML" id="html-2403.00540" aria-labelledby="html-2403.00540" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00540" title="Other formats" id="oth-2403.00540" aria-labelledby="oth-2403.00540">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Epsilon-Greedy Thompson Sampling to Bayesian Optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Do,+B">Bach Do</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Adebiyi,+T">Taiwo Adebiyi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+R">Ruda Zhang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/2403.00542" title="Abstract" id="2403.00542"> arXiv:2403.00542 </a> [<a href="/pdf/2403.00542" title="Download PDF" id="pdf-2403.00542" aria-labelledby="pdf-2403.00542">pdf</a>, <a href="/format/2403.00542" title="Other formats" id="oth-2403.00542" aria-labelledby="oth-2403.00542">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Machine Learning Training Optimization using the Barycentric Correction Procedure </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ramos-Pulido,+S">Sofia Ramos-Pulido</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hernandez-Gress,+N">Neil Hernandez-Gress</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ceballos-Cancino,+H+G">Hector G. Ceballos-Cancino</a> (Tecnologico de Monterrey, Mexico)</div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Computer Science & Information Technology (CS & IT) ISSN : 2231 - 5403 Volume 14, Number 04, February 2024 </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/2403.00550" title="Abstract" id="2403.00550"> arXiv:2403.00550 </a> [<a href="/pdf/2403.00550" title="Download PDF" id="pdf-2403.00550" aria-labelledby="pdf-2403.00550">pdf</a>, <a href="https://arxiv.org/html/2403.00550v1" title="View HTML" id="html-2403.00550" aria-labelledby="html-2403.00550" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00550" title="Other formats" id="oth-2403.00550" aria-labelledby="oth-2403.00550">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gavenski,+N">Nathan Gavenski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Luck,+M">Michael Luck</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rodrigues,+O">Odinaldo Rodrigues</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> his paper has been accepted in the demonstration track for the 23rd International Conference on Autonomous Agents and Multi-Agent Systems </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='item39'>[39]</a> <a href ="/abs/2403.00563" title="Abstract" id="2403.00563"> arXiv:2403.00563 </a> [<a href="/pdf/2403.00563" title="Download PDF" id="pdf-2403.00563" aria-labelledby="pdf-2403.00563">pdf</a>, <a href="https://arxiv.org/html/2403.00563v2" title="View HTML" id="html-2403.00563" aria-labelledby="html-2403.00563" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00563" title="Other formats" id="oth-2403.00563" aria-labelledby="oth-2403.00563">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Indirectly Parameterized Concrete Autoencoders </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nilsson,+A">Alfred Nilsson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wijk,+K">Klas Wijk</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gutha,+S+b+c">Sai bharath chandra Gutha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Englesson,+E">Erik Englesson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hotti,+A">Alexandra Hotti</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Saccardi,+C">Carlo Saccardi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kviman,+O">Oskar Kviman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lagergren,+J">Jens Lagergren</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vinuesa,+R">Ricardo Vinuesa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Azizpour,+H">Hossein Azizpour</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICML 2024 </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/2403.00564" title="Abstract" id="2403.00564"> arXiv:2403.00564 </a> [<a href="/pdf/2403.00564" title="Download PDF" id="pdf-2403.00564" aria-labelledby="pdf-2403.00564">pdf</a>, <a href="https://arxiv.org/html/2403.00564v2" title="View HTML" id="html-2403.00564" aria-labelledby="html-2403.00564" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00564" title="Other formats" id="oth-2403.00564" aria-labelledby="oth-2403.00564">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+S">Shengjie Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+S">Shaohuai Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ye,+W">Weirui Ye</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=You,+J">Jiacheng You</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+Y">Yang Gao</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 21 pages,10 figures </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='item41'>[41]</a> <a href ="/abs/2403.00574" title="Abstract" id="2403.00574"> arXiv:2403.00574 </a> [<a href="/pdf/2403.00574" title="Download PDF" id="pdf-2403.00574" aria-labelledby="pdf-2403.00574">pdf</a>, <a href="https://arxiv.org/html/2403.00574v1" title="View HTML" id="html-2403.00574" aria-labelledby="html-2403.00574" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00574" title="Other formats" id="oth-2403.00574" aria-labelledby="oth-2403.00574">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Inan,+T+T">Toki Tahmid Inan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+M">Mingrui Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shehu,+A">Amarda Shehu</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/2403.00625" title="Abstract" id="2403.00625"> arXiv:2403.00625 </a> [<a href="/pdf/2403.00625" title="Download PDF" id="pdf-2403.00625" aria-labelledby="pdf-2403.00625">pdf</a>, <a href="https://arxiv.org/html/2403.00625v1" title="View HTML" id="html-2403.00625" aria-labelledby="html-2403.00625" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00625" title="Other formats" id="oth-2403.00625" aria-labelledby="oth-2403.00625">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Yixuan Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+F">Feng Zhou</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computers and Society (cs.CY) </div> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2403.00642" title="Abstract" id="2403.00642"> arXiv:2403.00642 </a> [<a href="/pdf/2403.00642" title="Download PDF" id="pdf-2403.00642" aria-labelledby="pdf-2403.00642">pdf</a>, <a href="https://arxiv.org/html/2403.00642v2" title="View HTML" id="html-2403.00642" aria-labelledby="html-2403.00642" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00642" title="Other formats" id="oth-2403.00642" aria-labelledby="oth-2403.00642">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Rethinking The Uniformity Metric in Self-Supervised Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Fang,+X">Xianghong Fang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+J">Jian Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+Q">Qiang Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+B">Benyou Wang</a></div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> ICLR 2024 </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='item44'>[44]</a> <a href ="/abs/2403.00646" title="Abstract" id="2403.00646"> arXiv:2403.00646 </a> [<a href="/pdf/2403.00646" title="Download PDF" id="pdf-2403.00646" aria-labelledby="pdf-2403.00646">pdf</a>, <a href="https://arxiv.org/html/2403.00646v1" title="View HTML" id="html-2403.00646" aria-labelledby="html-2403.00646" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00646" title="Other formats" id="oth-2403.00646" aria-labelledby="oth-2403.00646">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Stability-Certified Learning of Control Systems with Quadratic Nonlinearities </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Duff,+I+P">Igor Pontes Duff</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Goyal,+P">Pawan Goyal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Benner,+P">Peter Benner</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 4 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Dynamical Systems (math.DS); Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2403.00669" title="Abstract" id="2403.00669"> arXiv:2403.00669 </a> [<a href="/pdf/2403.00669" title="Download PDF" id="pdf-2403.00669" aria-labelledby="pdf-2403.00669">pdf</a>, <a href="https://arxiv.org/html/2403.00669v2" title="View HTML" id="html-2403.00669" aria-labelledby="html-2403.00669" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00669" title="Other formats" id="oth-2403.00669" aria-labelledby="oth-2403.00669">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Saimon,+A+I">Amirul Islam Saimon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yangue,+E">Emmanuel Yangue</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yue,+X">Xiaowei Yue</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kong,+Z+J">Zhenyu James Kong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+C">Chenang Liu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 55 pages, 7 figures, 10 Tables, Published in IISE Transactions </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> IISE Transactions, 1-44, 2024 </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='item46'>[46]</a> <a href ="/abs/2403.00673" title="Abstract" id="2403.00673"> arXiv:2403.00673 </a> [<a href="/pdf/2403.00673" title="Download PDF" id="pdf-2403.00673" aria-labelledby="pdf-2403.00673">pdf</a>, <a href="https://arxiv.org/html/2403.00673v2" title="View HTML" id="html-2403.00673" aria-labelledby="html-2403.00673" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00673" title="Other formats" id="oth-2403.00673" aria-labelledby="oth-2403.00673">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Snapshot Reinforcement Learning: Leveraging Prior Trajectories for Efficiency </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+Y">Yanxiao Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qian,+Y">Yangge Qian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+T">Tianyi Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shan,+J">Jingyang Shan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qin,+X">Xiaolin Qin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Under review </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='item47'>[47]</a> <a href ="/abs/2403.00675" title="Abstract" id="2403.00675"> arXiv:2403.00675 </a> [<a href="/pdf/2403.00675" title="Download PDF" id="pdf-2403.00675" aria-labelledby="pdf-2403.00675">pdf</a>, <a href="https://arxiv.org/html/2403.00675v2" title="View HTML" id="html-2403.00675" aria-labelledby="html-2403.00675" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00675" title="Other formats" id="oth-2403.00675" aria-labelledby="oth-2403.00675">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+Y">Yifan Lin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yuhao Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+E">Enlu Zhou</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item48'>[48]</a> <a href ="/abs/2403.00680" title="Abstract" id="2403.00680"> arXiv:2403.00680 </a> [<a href="/pdf/2403.00680" title="Download PDF" id="pdf-2403.00680" aria-labelledby="pdf-2403.00680">pdf</a>, <a href="https://arxiv.org/html/2403.00680v2" title="View HTML" id="html-2403.00680" aria-labelledby="html-2403.00680" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00680" title="Other formats" id="oth-2403.00680" aria-labelledby="oth-2403.00680">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Scalable Learning of Item Response Theory Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Frick,+S">Susanne Frick</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Krivo%C5%A1ija,+A">Amer Krivo拧ija</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Munteanu,+A">Alexander Munteanu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Published in AISTATS 2024. V2: References updated </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); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item49'>[49]</a> <a href ="/abs/2403.00715" title="Abstract" id="2403.00715"> arXiv:2403.00715 </a> [<a href="/pdf/2403.00715" title="Download PDF" id="pdf-2403.00715" aria-labelledby="pdf-2403.00715">pdf</a>, <a href="/format/2403.00715" title="Other formats" id="oth-2403.00715" aria-labelledby="oth-2403.00715">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ito,+S">Shinji Ito</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tsuchiya,+T">Taira Tsuchiya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Honda,+J">Junya Honda</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/2403.00720" title="Abstract" id="2403.00720"> arXiv:2403.00720 </a> [<a href="/pdf/2403.00720" title="Download PDF" id="pdf-2403.00720" aria-labelledby="pdf-2403.00720">pdf</a>, <a href="https://arxiv.org/html/2403.00720v2" title="View HTML" id="html-2403.00720" aria-labelledby="html-2403.00720" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2403.00720" title="Other formats" id="oth-2403.00720" aria-labelledby="oth-2403.00720">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Subhomogeneous Deep Equilibrium Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sittoni,+P">Pietro Sittoni</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tudisco,+F">Francesco Tudisco</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; 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