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Machine Learning Mar 2025
<!DOCTYPE html> <html lang="en"> <head> <title>Machine Learning Mar 2025</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='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/current?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/current?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/current?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/2503.00028" title="Abstract" id="2503.00028"> arXiv:2503.00028 </a> [<a href="/pdf/2503.00028" title="Download PDF" id="pdf-2503.00028" aria-labelledby="pdf-2503.00028">pdf</a>, <a href="https://arxiv.org/html/2503.00028v1" title="View HTML" id="html-2503.00028" aria-labelledby="html-2503.00028" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00028" title="Other formats" id="oth-2503.00028" aria-labelledby="oth-2503.00028">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Genetics-Driven Personalized Disease Progression Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+H">Haoyu Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dey,+S">Sanjoy Dey</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Meyer,+P">Pablo Meyer</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='item2'>[2]</a> <a href ="/abs/2503.00029" title="Abstract" id="2503.00029"> arXiv:2503.00029 </a> [<a href="/pdf/2503.00029" title="Download PDF" id="pdf-2503.00029" aria-labelledby="pdf-2503.00029">pdf</a>, <a href="https://arxiv.org/html/2503.00029v1" title="View HTML" id="html-2503.00029" aria-labelledby="html-2503.00029" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00029" title="Other formats" id="oth-2503.00029" aria-labelledby="oth-2503.00029">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Streaming Looking Ahead with Token-level Self-reward </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+H">Hongming Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hong,+R">Ruixin Hong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+D">Dong Yu</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='item3'>[3]</a> <a href ="/abs/2503.00030" title="Abstract" id="2503.00030"> arXiv:2503.00030 </a> [<a href="/pdf/2503.00030" title="Download PDF" id="pdf-2503.00030" aria-labelledby="pdf-2503.00030">pdf</a>, <a href="https://arxiv.org/html/2503.00030v1" title="View HTML" id="html-2503.00030" aria-labelledby="html-2503.00030" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00030" title="Other formats" id="oth-2503.00030" aria-labelledby="oth-2503.00030">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Game-Theoretic Regularized Self-Play Alignment of Large Language Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Tang,+X">Xiaohang Tang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yoon,+S">Sangwoong Yoon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Son,+S">Seongho Son</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yuan,+H">Huizhuo Yuan</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=Bogunovic,+I">Ilija Bogunovic</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Preprint </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2503.00031" title="Abstract" id="2503.00031"> arXiv:2503.00031 </a> [<a href="/pdf/2503.00031" title="Download PDF" id="pdf-2503.00031" aria-labelledby="pdf-2503.00031">pdf</a>, <a href="https://arxiv.org/html/2503.00031v1" title="View HTML" id="html-2503.00031" aria-labelledby="html-2503.00031" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00031" title="Other formats" id="oth-2503.00031" aria-labelledby="oth-2503.00031">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Efficient Test-Time Scaling via Self-Calibration </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+C">Chengsong Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+L">Langlin Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Leng,+J">Jixuan Leng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+J">Jiacheng Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+J">Jiaxin Huang</a></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='item5'>[5]</a> <a href ="/abs/2503.00033" title="Abstract" id="2503.00033"> arXiv:2503.00033 </a> [<a href="/pdf/2503.00033" title="Download PDF" id="pdf-2503.00033" aria-labelledby="pdf-2503.00033">pdf</a>, <a href="https://arxiv.org/html/2503.00033v1" title="View HTML" id="html-2503.00033" aria-labelledby="html-2503.00033" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00033" title="Other formats" id="oth-2503.00033" aria-labelledby="oth-2503.00033">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> optimizn: a Python Library for Developing Customized Optimization Algorithms </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sathiya,+A">Akshay Sathiya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pandey,+R">Rohit Pandey</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='item6'>[6]</a> <a href ="/abs/2503.00034" title="Abstract" id="2503.00034"> arXiv:2503.00034 </a> [<a href="/pdf/2503.00034" title="Download PDF" id="pdf-2503.00034" aria-labelledby="pdf-2503.00034">pdf</a>, <a href="https://arxiv.org/html/2503.00034v1" title="View HTML" id="html-2503.00034" aria-labelledby="html-2503.00034" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00034" title="Other formats" id="oth-2503.00034" aria-labelledby="oth-2503.00034">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MergeIT: From Selection to Merging for Efficient Instruction Tuning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cai,+H">Hongyi Cai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fu,+Y">Yuqian Fu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fu,+H">Hongming Fu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+B">Bo Zhao</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2503.00094" title="Abstract" id="2503.00094"> arXiv:2503.00094 </a> [<a href="/pdf/2503.00094" title="Download PDF" id="pdf-2503.00094" aria-labelledby="pdf-2503.00094">pdf</a>, <a href="https://arxiv.org/html/2503.00094v1" title="View HTML" id="html-2503.00094" aria-labelledby="html-2503.00094" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00094" title="Other formats" id="oth-2503.00094" aria-labelledby="oth-2503.00094">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Houdouin,+P">Pierre Houdouin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ruiz,+M">Manuel Ruiz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Saludjian,+L">Lucas Saludjian</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 6 pages, 7 figures, conference </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='item8'>[8]</a> <a href ="/abs/2503.00127" title="Abstract" id="2503.00127"> arXiv:2503.00127 </a> [<a href="/pdf/2503.00127" title="Download PDF" id="pdf-2503.00127" aria-labelledby="pdf-2503.00127">pdf</a>, <a href="/format/2503.00127" title="Other formats" id="oth-2503.00127" aria-labelledby="oth-2503.00127">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> DISCO: Internal Evaluation of Density-Based Clustering </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Beer,+A">Anna Beer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Krieger,+L">Lena Krieger</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Weber,+P">Pascal Weber</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ritzert,+M">Martin Ritzert</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Assent,+I">Ira Assent</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Plant,+C">Claudia Plant</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='item9'>[9]</a> <a href ="/abs/2503.00152" title="Abstract" id="2503.00152"> arXiv:2503.00152 </a> [<a href="/pdf/2503.00152" title="Download PDF" id="pdf-2503.00152" aria-labelledby="pdf-2503.00152">pdf</a>, <a href="https://arxiv.org/html/2503.00152v1" title="View HTML" id="html-2503.00152" aria-labelledby="html-2503.00152" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00152" title="Other formats" id="oth-2503.00152" aria-labelledby="oth-2503.00152">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yan,+K">Keqiang Yan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+X">Xiner Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ling,+H">Hongyi Ling</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ashen,+K">Kenna Ashen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Edwards,+C">Carl Edwards</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Arr%C3%B3yave,+R">Raymundo Arr贸yave</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zitnik,+M">Marinka Zitnik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ji,+H">Heng Ji</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qian,+X">Xiaofeng Qian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qian,+X">Xiaoning Qian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ji,+S">Shuiwang Ji</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This paper has been accepted as a NeurIPS 2024 Poster </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Materials Science (cond-mat.mtrl-sci) </div> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2503.00174" title="Abstract" id="2503.00174"> arXiv:2503.00174 </a> [<a href="/pdf/2503.00174" title="Download PDF" id="pdf-2503.00174" aria-labelledby="pdf-2503.00174">pdf</a>, <a href="https://arxiv.org/html/2503.00174v1" title="View HTML" id="html-2503.00174" aria-labelledby="html-2503.00174" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00174" title="Other formats" id="oth-2503.00174" aria-labelledby="oth-2503.00174">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal Transfer Learning for Missing Not-at-Random Matrix Completion </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Jalan,+A">Akhil Jalan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jedra,+Y">Yassir Jedra</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mazumdar,+A">Arya Mazumdar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mukherjee,+S+S">Soumendu Sundar Mukherjee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sarkar,+P">Purnamrita Sarkar</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='item11'>[11]</a> <a href ="/abs/2503.00177" title="Abstract" id="2503.00177"> arXiv:2503.00177 </a> [<a href="/pdf/2503.00177" title="Download PDF" id="pdf-2503.00177" aria-labelledby="pdf-2503.00177">pdf</a>, <a href="https://arxiv.org/html/2503.00177v1" title="View HTML" id="html-2503.00177" aria-labelledby="html-2503.00177" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00177" title="Other formats" id="oth-2503.00177" aria-labelledby="oth-2503.00177">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Steering Large Language Model Activations in Sparse Spaces </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bayat,+R">Reza Bayat</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rahimi-Kalahroudi,+A">Ali Rahimi-Kalahroudi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pezeshki,+M">Mohammad Pezeshki</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chandar,+S">Sarath Chandar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vincent,+P">Pascal Vincent</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='item12'>[12]</a> <a href ="/abs/2503.00205" title="Abstract" id="2503.00205"> arXiv:2503.00205 </a> [<a href="/pdf/2503.00205" title="Download PDF" id="pdf-2503.00205" aria-labelledby="pdf-2503.00205">pdf</a>, <a href="https://arxiv.org/html/2503.00205v1" title="View HTML" id="html-2503.00205" aria-labelledby="html-2503.00205" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00205" title="Other formats" id="oth-2503.00205" aria-labelledby="oth-2503.00205">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+J">Jian Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cao,+W">Weidong Cao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+J">Junyi Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+X">Xuan Zhang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICLR 2025 camera ready </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/2503.00206" title="Abstract" id="2503.00206"> arXiv:2503.00206 </a> [<a href="/pdf/2503.00206" title="Download PDF" id="pdf-2503.00206" aria-labelledby="pdf-2503.00206">pdf</a>, <a href="https://arxiv.org/html/2503.00206v1" title="View HTML" id="html-2503.00206" aria-labelledby="html-2503.00206" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00206" title="Other formats" id="oth-2503.00206" aria-labelledby="oth-2503.00206">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approach </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mysore,+N">Naveen Mysore</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Under review for RLC 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2503.00210" title="Abstract" id="2503.00210"> arXiv:2503.00210 </a> [<a href="/pdf/2503.00210" title="Download PDF" id="pdf-2503.00210" aria-labelledby="pdf-2503.00210">pdf</a>, <a href="https://arxiv.org/html/2503.00210v1" title="View HTML" id="html-2503.00210" aria-labelledby="html-2503.00210" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00210" title="Other formats" id="oth-2503.00210" aria-labelledby="oth-2503.00210">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response Prediction </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Fan,+W">Wenrui Fan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rizky,+L+M+R">L. M. Riza Rizky</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+J">Jiayang Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+C">Chen Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lu,+H">Haiping Lu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Teh,+K">Kevin Teh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Selvarajah,+D">Dinesh Selvarajah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+S">Shuo Zhou</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); Signal Processing (eess.SP) </div> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2503.00229" title="Abstract" id="2503.00229"> arXiv:2503.00229 </a> [<a href="/pdf/2503.00229" title="Download PDF" id="pdf-2503.00229" aria-labelledby="pdf-2503.00229">pdf</a>, <a href="https://arxiv.org/html/2503.00229v1" title="View HTML" id="html-2503.00229" aria-labelledby="html-2503.00229" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00229" title="Other formats" id="oth-2503.00229" aria-labelledby="oth-2503.00229">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Armijo Line-search Makes (Stochastic) Gradient Descent Go Fast </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Vaswani,+S">Sharan Vaswani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Babanezhad,+R">Reza Babanezhad</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 33 pages </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='item16'>[16]</a> <a href ="/abs/2503.00234" title="Abstract" id="2503.00234"> arXiv:2503.00234 </a> [<a href="/pdf/2503.00234" title="Download PDF" id="pdf-2503.00234" aria-labelledby="pdf-2503.00234">pdf</a>, <a href="https://arxiv.org/html/2503.00234v1" title="View HTML" id="html-2503.00234" aria-labelledby="html-2503.00234" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00234" title="Other formats" id="oth-2503.00234" aria-labelledby="oth-2503.00234">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards Fairness for the Right Reasons: Using Saliency Maps to Evaluate Bias Removal in Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sztukiewicz,+L">Lukasz Sztukiewicz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=St%C4%99pka,+I">Ignacy St臋pka</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wili%C5%84ski,+M">Micha艂 Wili艅ski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stefanowski,+J">Jerzy Stefanowski</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) </div> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2503.00240" title="Abstract" id="2503.00240"> arXiv:2503.00240 </a> [<a href="/pdf/2503.00240" title="Download PDF" id="pdf-2503.00240" aria-labelledby="pdf-2503.00240">pdf</a>, <a href="https://arxiv.org/html/2503.00240v1" title="View HTML" id="html-2503.00240" aria-labelledby="html-2503.00240" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00240" title="Other formats" id="oth-2503.00240" aria-labelledby="oth-2503.00240">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> 1-Lipschitz Network Initialization for Certifiably Robust Classification Applications: A Decay Problem </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Juston,+M+F+R">Marius F. R. Juston</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Norris,+W+R">William R. Norris</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nottage,+D">Dustin Nottage</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Soylemezoglu,+A">Ahmet Soylemezoglu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 8 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2503.00245" title="Abstract" id="2503.00245"> arXiv:2503.00245 </a> [<a href="/pdf/2503.00245" title="Download PDF" id="pdf-2503.00245" aria-labelledby="pdf-2503.00245">pdf</a>, <a href="/format/2503.00245" title="Other formats" id="oth-2503.00245" aria-labelledby="oth-2503.00245">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> CoSMoEs: Compact Sparse Mixture of Experts </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Huber,+P">Patrick Huber</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shrivastava,+A">Akshat Shrivastava</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chang,+E">Ernie Chang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sankar,+C">Chinnadhurai Sankar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Aly,+A">Ahmed Aly</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sagar,+A">Adithya Sagar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 8 figures </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='item19'>[19]</a> <a href ="/abs/2503.00268" title="Abstract" id="2503.00268"> arXiv:2503.00268 </a> [<a href="/pdf/2503.00268" title="Download PDF" id="pdf-2503.00268" aria-labelledby="pdf-2503.00268">pdf</a>, <a href="https://arxiv.org/html/2503.00268v1" title="View HTML" id="html-2503.00268" aria-labelledby="html-2503.00268" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00268" title="Other formats" id="oth-2503.00268" aria-labelledby="oth-2503.00268">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Input Specific Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Jadoon,+A+A">Asghar A. Jadoon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Seidl,+D+T">D. Thomas Seidl</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jones,+R+E">Reese E. Jones</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fuhg,+J+N">Jan N. Fuhg</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Neural and Evolutionary Computing (cs.NE) </div> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2503.00269" title="Abstract" id="2503.00269"> arXiv:2503.00269 </a> [<a href="/pdf/2503.00269" title="Download PDF" id="pdf-2503.00269" aria-labelledby="pdf-2503.00269">pdf</a>, <a href="https://arxiv.org/html/2503.00269v1" title="View HTML" id="html-2503.00269" aria-labelledby="html-2503.00269" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00269" title="Other formats" id="oth-2503.00269" aria-labelledby="oth-2503.00269">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Penny-Dimri,+J+C">Jahan C. Penny-Dimri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bachmann,+M">Magdalena Bachmann</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cooke,+W+R">William R. Cooke</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mathewlynn,+S">Sam Mathewlynn</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dockree,+S">Samuel Dockree</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tolladay,+J">John Tolladay</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kossen,+J">Jannik Kossen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+L">Lin Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gal,+Y">Yarin Gal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jones,+G+D">Gabriel Davis Jones</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 15 pages, 6 tables </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); Computers and Society (cs.CY) </div> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2503.00286" title="Abstract" id="2503.00286"> arXiv:2503.00286 </a> [<a href="/pdf/2503.00286" title="Download PDF" id="pdf-2503.00286" aria-labelledby="pdf-2503.00286">pdf</a>, <a href="https://arxiv.org/html/2503.00286v1" title="View HTML" id="html-2503.00286" aria-labelledby="html-2503.00286" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00286" title="Other formats" id="oth-2503.00286" aria-labelledby="oth-2503.00286">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Unified Framework for Heterogeneous Semi-supervised Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Heidari,+M">Marzi Heidari</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alchihabi,+A">Abdullah Alchihabi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yan,+H">Hao Yan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guo,+Y">Yuhong Guo</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2503.00299" title="Abstract" id="2503.00299"> arXiv:2503.00299 </a> [<a href="/pdf/2503.00299" title="Download PDF" id="pdf-2503.00299" aria-labelledby="pdf-2503.00299">pdf</a>, <a href="https://arxiv.org/html/2503.00299v1" title="View HTML" id="html-2503.00299" aria-labelledby="html-2503.00299" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00299" title="Other formats" id="oth-2503.00299" aria-labelledby="oth-2503.00299">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Hidden Convexity of Fair PCA and Fast Solver via Eigenvalue Optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Shen,+J">Junhui Shen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Davis,+A+J">Aaron J. Davis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lu,+D">Ding Lu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bai,+Z">Zhaojun Bai</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); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2503.00300" title="Abstract" id="2503.00300"> arXiv:2503.00300 </a> [<a href="/pdf/2503.00300" title="Download PDF" id="pdf-2503.00300" aria-labelledby="pdf-2503.00300">pdf</a>, <a href="https://arxiv.org/html/2503.00300v1" title="View HTML" id="html-2503.00300" aria-labelledby="html-2503.00300" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00300" title="Other formats" id="oth-2503.00300" aria-labelledby="oth-2503.00300">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Cauchy Random Features for Operator Learning in Sobolev Space </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Liao,+C">Chunyang Liao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Needell,+D">Deanna Needell</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schaeffer,+H">Hayden Schaeffer</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 31 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Numerical Analysis (math.NA); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2503.00307" title="Abstract" id="2503.00307"> arXiv:2503.00307 </a> [<a href="/pdf/2503.00307" title="Download PDF" id="pdf-2503.00307" aria-labelledby="pdf-2503.00307">pdf</a>, <a href="https://arxiv.org/html/2503.00307v1" title="View HTML" id="html-2503.00307" aria-labelledby="html-2503.00307" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00307" title="Other formats" id="oth-2503.00307" aria-labelledby="oth-2503.00307">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Remasking Discrete Diffusion Models with Inference-Time Scaling </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+G">Guanghan Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schiff,+Y">Yair Schiff</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sahoo,+S+S">Subham Sekhar Sahoo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kuleshov,+V">Volodymyr Kuleshov</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Project page: <a href="https://remdm.github.io" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2503.00317" title="Abstract" id="2503.00317"> arXiv:2503.00317 </a> [<a href="/pdf/2503.00317" title="Download PDF" id="pdf-2503.00317" aria-labelledby="pdf-2503.00317">pdf</a>, <a href="https://arxiv.org/html/2503.00317v1" title="View HTML" id="html-2503.00317" aria-labelledby="html-2503.00317" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00317" title="Other formats" id="oth-2503.00317" aria-labelledby="oth-2503.00317">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+Z">Zhaoxi Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+F">Fei Wang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Numerical Analysis (math.NA); Computational Physics (physics.comp-ph) </div> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2503.00323" title="Abstract" id="2503.00323"> arXiv:2503.00323 </a> [<a href="/pdf/2503.00323" title="Download PDF" id="pdf-2503.00323" aria-labelledby="pdf-2503.00323">pdf</a>, <a href="https://arxiv.org/html/2503.00323v1" title="View HTML" id="html-2503.00323" aria-labelledby="html-2503.00323" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00323" title="Other formats" id="oth-2503.00323" aria-labelledby="oth-2503.00323">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> FLStore: Efficient Federated Learning Storage for non-training workloads </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Khan,+A+F">Ahmad Faraz Khan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fountain,+S">Samuel Fountain</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abdelmoniem,+A+M">Ahmed M. Abdelmoniem</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Butt,+A+R">Ali R. Butt</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Anwar,+A">Ali Anwar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 11 pages, 19 figures, 2 tables This paper has been accepted at the The Eighth Annual Conference on Machine Learning and Systems (MLSys 2025) </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='item27'>[27]</a> <a href ="/abs/2503.00331" title="Abstract" id="2503.00331"> arXiv:2503.00331 </a> [<a href="/pdf/2503.00331" title="Download PDF" id="pdf-2503.00331" aria-labelledby="pdf-2503.00331">pdf</a>, <a href="/format/2503.00331" title="Other formats" id="oth-2503.00331" aria-labelledby="oth-2503.00331">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Naeini,+H+K">Hajar Kazemi Naeini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shomali,+R">Roya Shomali</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pishahang,+A">Abolhassan Pishahang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hasanzadeh,+H">Hamidreza Hasanzadeh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mohammadi,+M">Mahdieh Mohammadi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Asadi,+S">Saeid Asadi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lonbar,+A+G">Ahmad Gholizadeh Lonbar</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='item28'>[28]</a> <a href ="/abs/2503.00334" title="Abstract" id="2503.00334"> arXiv:2503.00334 </a> [<a href="/pdf/2503.00334" title="Download PDF" id="pdf-2503.00334" aria-labelledby="pdf-2503.00334">pdf</a>, <a href="https://arxiv.org/html/2503.00334v1" title="View HTML" id="html-2503.00334" aria-labelledby="html-2503.00334" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00334" title="Other formats" id="oth-2503.00334" aria-labelledby="oth-2503.00334">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Dai,+Q">Quanyu Dai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xiao,+J">Jiaren Xiao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+Z">Zhaocheng Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhu,+J">Jieming Zhu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Luo,+C">Chengxiao Luo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+X">Xiao-Ming Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dong,+Z">Zhenhua Dong</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted by WWW2025 </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> THE ACM WEB CONFERENCE 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2503.00345" title="Abstract" id="2503.00345"> arXiv:2503.00345 </a> [<a href="/pdf/2503.00345" title="Download PDF" id="pdf-2503.00345" aria-labelledby="pdf-2503.00345">pdf</a>, <a href="https://arxiv.org/html/2503.00345v1" title="View HTML" id="html-2503.00345" aria-labelledby="html-2503.00345" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00345" title="Other formats" id="oth-2503.00345" aria-labelledby="oth-2503.00345">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards Understanding the Benefit of Multitask Representation Learning in Decision Process </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lu,+R">Rui Lu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yue,+Y">Yang Yue</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+A">Andrew Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+S">Simon Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+G">Gao Huang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> arXiv admin note: substantial text overlap with <a href="https://arxiv.org/abs/2205.15701" data-arxiv-id="2205.15701" class="link-https">arXiv:2205.15701</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/2503.00378" title="Abstract" id="2503.00378"> arXiv:2503.00378 </a> [<a href="/pdf/2503.00378" title="Download PDF" id="pdf-2503.00378" aria-labelledby="pdf-2503.00378">pdf</a>, <a href="https://arxiv.org/html/2503.00378v1" title="View HTML" id="html-2503.00378" aria-labelledby="html-2503.00378" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00378" title="Other formats" id="oth-2503.00378" aria-labelledby="oth-2503.00378">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Conditioning on Local Statistics for Scalable Heterogeneous Federated Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Br%C3%A4nnvall,+R">Rickard Br盲nnvall</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 7 pages, 2 figures, 7 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC) </div> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/2503.00379" title="Abstract" id="2503.00379"> arXiv:2503.00379 </a> [<a href="/pdf/2503.00379" title="Download PDF" id="pdf-2503.00379" aria-labelledby="pdf-2503.00379">pdf</a>, <a href="https://arxiv.org/html/2503.00379v1" title="View HTML" id="html-2503.00379" aria-labelledby="html-2503.00379" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00379" title="Other formats" id="oth-2503.00379" aria-labelledby="oth-2503.00379">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Improving internal cluster quality evaluation in noisy Gaussian mixtures </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=de+Amorim,+R+C">Renato Cordeiro de Amorim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Makarenkov,+V">Vladimir Makarenkov</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/2503.00383" title="Abstract" id="2503.00383"> arXiv:2503.00383 </a> [<a href="/pdf/2503.00383" title="Download PDF" id="pdf-2503.00383" aria-labelledby="pdf-2503.00383">pdf</a>, <a href="https://arxiv.org/html/2503.00383v1" title="View HTML" id="html-2503.00383" aria-labelledby="html-2503.00383" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00383" title="Other formats" id="oth-2503.00383" aria-labelledby="oth-2503.00383">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xia,+S">Song Xia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+Y">Yi Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+W">Wenhan Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ding,+M">Meiwen Ding</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Z">Zhuo Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Duan,+L">Lingyu Duan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kot,+A+C">Alex C. Kot</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+X">Xudong Jiang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> accepted by CVPR2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2503.00392" title="Abstract" id="2503.00392"> arXiv:2503.00392 </a> [<a href="/pdf/2503.00392" title="Download PDF" id="pdf-2503.00392" aria-labelledby="pdf-2503.00392">pdf</a>, <a href="https://arxiv.org/html/2503.00392v1" title="View HTML" id="html-2503.00392" aria-labelledby="html-2503.00392" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00392" title="Other formats" id="oth-2503.00392" aria-labelledby="oth-2503.00392">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Progressive Sparse Attention: Algorithm and System Co-design for Efficient Attention in LLM Serving </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+Q">Qihui Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yin,+P">Peiqi Yin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zuo,+P">Pengfei Zuo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cheng,+J">James Cheng</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 6 figures </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/2503.00393" title="Abstract" id="2503.00393"> arXiv:2503.00393 </a> [<a href="/pdf/2503.00393" title="Download PDF" id="pdf-2503.00393" aria-labelledby="pdf-2503.00393">pdf</a>, <a href="https://arxiv.org/html/2503.00393v1" title="View HTML" id="html-2503.00393" aria-labelledby="html-2503.00393" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00393" title="Other formats" id="oth-2503.00393" aria-labelledby="oth-2503.00393">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Reservoir Network with Structural Plasticity for Human Activity Recognition </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zyarah,+A+M">Abdullah M. Zyarah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abdul-Hadi,+A+M">Alaa M. Abdul-Hadi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kudithipudi,+D">Dhireesha Kudithipudi</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='item35'>[35]</a> <a href ="/abs/2503.00407" title="Abstract" id="2503.00407"> arXiv:2503.00407 </a> [<a href="/pdf/2503.00407" title="Download PDF" id="pdf-2503.00407" aria-labelledby="pdf-2503.00407">pdf</a>, <a href="/format/2503.00407" title="Other formats" id="oth-2503.00407" aria-labelledby="oth-2503.00407">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Asynchronous Personalized Federated Learning through Global Memorization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wan,+F">Fan Wan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+Y">Yuchen Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qiu,+X">Xueqi Qiu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+R">Rui Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+L">Leyuan Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Miao,+X">Xingyu Miao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+T">Tianyu Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Duan,+H">Haoran Duan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Long,+Y">Yang Long</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Distributed, Parallel, and Cluster Computing (cs.DC) </div> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2503.00419" title="Abstract" id="2503.00419"> arXiv:2503.00419 </a> [<a href="/pdf/2503.00419" title="Download PDF" id="pdf-2503.00419" aria-labelledby="pdf-2503.00419">pdf</a>, <a href="https://arxiv.org/html/2503.00419v1" title="View HTML" id="html-2503.00419" aria-labelledby="html-2503.00419" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00419" title="Other formats" id="oth-2503.00419" aria-labelledby="oth-2503.00419">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J">Jing Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Yu-Jie Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+P">Peng Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+Z">Zhi-Hua Zhou</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='item37'>[37]</a> <a href ="/abs/2503.00426" title="Abstract" id="2503.00426"> arXiv:2503.00426 </a> [<a href="/pdf/2503.00426" title="Download PDF" id="pdf-2503.00426" aria-labelledby="pdf-2503.00426">pdf</a>, <a href="https://arxiv.org/html/2503.00426v1" title="View HTML" id="html-2503.00426" aria-labelledby="html-2503.00426" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00426" title="Other formats" id="oth-2503.00426" aria-labelledby="oth-2503.00426">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Auto-encoding Molecules: Graph-Matching Capabilities Matter </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cunow,+M">Magnus Cunow</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gro%C3%9Fmann,+G">Gerrit Gro脽mann</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='item38'>[38]</a> <a href ="/abs/2503.00458" title="Abstract" id="2503.00458"> arXiv:2503.00458 </a> [<a href="/pdf/2503.00458" title="Download PDF" id="pdf-2503.00458" aria-labelledby="pdf-2503.00458">pdf</a>, <a href="https://arxiv.org/html/2503.00458v1" title="View HTML" id="html-2503.00458" aria-labelledby="html-2503.00458" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00458" title="Other formats" id="oth-2503.00458" aria-labelledby="oth-2503.00458">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Using Machine Learning for move sequence visualization and generation in climbing </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Rimbot,+T">Thomas Rimbot</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jaggi,+M">Martin Jaggi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Barba,+L">Luis Barba</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='item39'>[39]</a> <a href ="/abs/2503.00470" title="Abstract" id="2503.00470"> arXiv:2503.00470 </a> [<a href="/pdf/2503.00470" title="Download PDF" id="pdf-2503.00470" aria-labelledby="pdf-2503.00470">pdf</a>, <a href="https://arxiv.org/html/2503.00470v1" title="View HTML" id="html-2503.00470" aria-labelledby="html-2503.00470" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00470" title="Other formats" id="oth-2503.00470" aria-labelledby="oth-2503.00470">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=He,+J">Junqi He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Yujie Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J">Jialu Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+T">Tao Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+P">Pan Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cai,+C">Chengjie Cai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+J">Jinxing Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+X">Xiao Lin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+X">Xiaohui Yang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Materials Science (cond-mat.mtrl-sci); Computer Vision and Pattern Recognition (cs.CV); Optics (physics.optics) </div> </div> </dd> <dt> <a name='item40'>[40]</a> <a href ="/abs/2503.00476" title="Abstract" id="2503.00476"> arXiv:2503.00476 </a> [<a href="/pdf/2503.00476" title="Download PDF" id="pdf-2503.00476" aria-labelledby="pdf-2503.00476">pdf</a>, <a href="https://arxiv.org/html/2503.00476v1" title="View HTML" id="html-2503.00476" aria-labelledby="html-2503.00476" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00476" title="Other formats" id="oth-2503.00476" aria-labelledby="oth-2503.00476">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Dong,+Y">Yicong Dong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=He,+R">Rundong He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+G">Guangyao Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+W">Wentao Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+Z">Zhongyi Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shi,+J">Jieming Shi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yin,+Y">Yilong Yin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 10 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='item41'>[41]</a> <a href ="/abs/2503.00479" title="Abstract" id="2503.00479"> arXiv:2503.00479 </a> [<a href="/pdf/2503.00479" title="Download PDF" id="pdf-2503.00479" aria-labelledby="pdf-2503.00479">pdf</a>, <a href="https://arxiv.org/html/2503.00479v1" title="View HTML" id="html-2503.00479" aria-labelledby="html-2503.00479" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00479" title="Other formats" id="oth-2503.00479" aria-labelledby="oth-2503.00479">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gray,+A">Andy Gray</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rahat,+A">Alma Rahat</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Crick,+T">Tom Crick</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lindsay,+S">Stephen Lindsay</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Information Retrieval (cs.IR); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item42'>[42]</a> <a href ="/abs/2503.00485" title="Abstract" id="2503.00485"> arXiv:2503.00485 </a> [<a href="/pdf/2503.00485" title="Download PDF" id="pdf-2503.00485" aria-labelledby="pdf-2503.00485">pdf</a>, <a href="https://arxiv.org/html/2503.00485v1" title="View HTML" id="html-2503.00485" aria-labelledby="html-2503.00485" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00485" title="Other formats" id="oth-2503.00485" aria-labelledby="oth-2503.00485">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Homomorphism Expressivity of Spectral Invariant Graph Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gai,+J">Jingchu Gai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+Y">Yiheng Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+B">Bohang Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Maron,+H">Haggai Maron</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+L">Liwei Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 42 pages </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> ICLR 2025 Oral </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='item43'>[43]</a> <a href ="/abs/2503.00499" title="Abstract" id="2503.00499"> arXiv:2503.00499 </a> [<a href="/pdf/2503.00499" title="Download PDF" id="pdf-2503.00499" aria-labelledby="pdf-2503.00499">pdf</a>, <a href="https://arxiv.org/html/2503.00499v1" title="View HTML" id="html-2503.00499" aria-labelledby="html-2503.00499" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00499" title="Other formats" id="oth-2503.00499" aria-labelledby="oth-2503.00499">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Shaping Laser Pulses with Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Capuano,+F">Francesco Capuano</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Peceli,+D">Davorin Peceli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tiboni,+G">Gabriele Tiboni</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Computational Physics (physics.comp-ph); Optics (physics.optics) </div> </div> </dd> <dt> <a name='item44'>[44]</a> <a href ="/abs/2503.00507" title="Abstract" id="2503.00507"> arXiv:2503.00507 </a> [<a href="/pdf/2503.00507" title="Download PDF" id="pdf-2503.00507" aria-labelledby="pdf-2503.00507">pdf</a>, <a href="https://arxiv.org/html/2503.00507v2" title="View HTML" id="html-2503.00507" aria-labelledby="html-2503.00507" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00507" title="Other formats" id="oth-2503.00507" aria-labelledby="oth-2503.00507">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Projection Head is Secretly an Information Bottleneck </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ouyang,+Z">Zhuo Ouyang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+K">Kaiwen Hu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Q">Qi Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yifei Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yisen Wang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Information Theory (cs.IT) </div> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2503.00509" title="Abstract" id="2503.00509"> arXiv:2503.00509 </a> [<a href="/pdf/2503.00509" title="Download PDF" id="pdf-2503.00509" aria-labelledby="pdf-2503.00509">pdf</a>, <a href="https://arxiv.org/html/2503.00509v1" title="View HTML" id="html-2503.00509" aria-labelledby="html-2503.00509" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00509" title="Other formats" id="oth-2503.00509" aria-labelledby="oth-2503.00509">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Functional multi-armed bandit and the best function identification problems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Dorn,+Y">Yuriy Dorn</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Katrutsa,+A">Aleksandr Katrutsa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Latypov,+I">Ilgam Latypov</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Soboleva,+A">Anastasiia Soboleva</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); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2503.00522" title="Abstract" id="2503.00522"> arXiv:2503.00522 </a> [<a href="/pdf/2503.00522" title="Download PDF" id="pdf-2503.00522" aria-labelledby="pdf-2503.00522">pdf</a>, <a href="https://arxiv.org/html/2503.00522v1" title="View HTML" id="html-2503.00522" aria-labelledby="html-2503.00522" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00522" title="Other formats" id="oth-2503.00522" aria-labelledby="oth-2503.00522">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Periodic Materials Generation using Text-Guided Joint Diffusion Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Das,+K">Kishalay Das</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Khastagir,+S">Subhojyoti Khastagir</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=Lee,+S">Seung-Cheol Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bhattacharjee,+S">Satadeep Bhattacharjee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ganguly,+N">Niloy Ganguly</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICLR 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Materials Science (cond-mat.mtrl-sci) </div> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/2503.00524" title="Abstract" id="2503.00524"> arXiv:2503.00524 </a> [<a href="/pdf/2503.00524" title="Download PDF" id="pdf-2503.00524" aria-labelledby="pdf-2503.00524">pdf</a>, <a href="https://arxiv.org/html/2503.00524v1" title="View HTML" id="html-2503.00524" aria-labelledby="html-2503.00524" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00524" title="Other formats" id="oth-2503.00524" aria-labelledby="oth-2503.00524">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Blessing,+D">Denis Blessing</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jia,+X">Xiaogang Jia</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Neumann,+G">Gerhard Neumann</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item48'>[48]</a> <a href ="/abs/2503.00528" title="Abstract" id="2503.00528"> arXiv:2503.00528 </a> [<a href="/pdf/2503.00528" title="Download PDF" id="pdf-2503.00528" aria-labelledby="pdf-2503.00528">pdf</a>, <a href="https://arxiv.org/html/2503.00528v1" title="View HTML" id="html-2503.00528" aria-labelledby="html-2503.00528" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00528" title="Other formats" id="oth-2503.00528" aria-labelledby="oth-2503.00528">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Efficient Prompting for Continual Adaptation to Missing Modalities </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Guo,+Z">Zirun Guo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+S">Shulei Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lin,+W">Wang Lin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yan,+W">Weicai Yan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+Y">Yangyang Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jin,+T">Tao Jin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to NAACL 2025 Main </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='item49'>[49]</a> <a href ="/abs/2503.00535" title="Abstract" id="2503.00535"> arXiv:2503.00535 </a> [<a href="/pdf/2503.00535" title="Download PDF" id="pdf-2503.00535" aria-labelledby="pdf-2503.00535">pdf</a>, <a href="https://arxiv.org/html/2503.00535v1" title="View HTML" id="html-2503.00535" aria-labelledby="html-2503.00535" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00535" title="Other formats" id="oth-2503.00535" aria-labelledby="oth-2503.00535">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> What Makes a Good Diffusion Planner for Decision Making? </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lu,+H">Haofei Lu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+D">Dongqi Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shen,+Y">Yifei Shen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+D">Dongsheng Li</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> ICLR 2025 (Spotlight), Code: <a href="https://github.com/Josh00-Lu/DiffusionVeteran" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item50'>[50]</a> <a href ="/abs/2503.00537" title="Abstract" id="2503.00537"> arXiv:2503.00537 </a> [<a href="/pdf/2503.00537" title="Download PDF" id="pdf-2503.00537" aria-labelledby="pdf-2503.00537">pdf</a>, <a href="https://arxiv.org/html/2503.00537v1" title="View HTML" id="html-2503.00537" aria-labelledby="html-2503.00537" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.00537" title="Other formats" id="oth-2503.00537" aria-labelledby="oth-2503.00537">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Scalable Reinforcement Learning for Virtual Machine Scheduling </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sheng,+J">Junjie Sheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+J">Jiehao Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cui,+H">Haochuan Cui</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+Y">Yiqiu Hu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+W">Wenli Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhu,+L">Lei Zhu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Peng,+Q">Qian Peng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+W">Wenhao Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+X">Xiangfeng Wang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 23 pages, 12 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> </dl> <div class='paging'>Total of 2473 entries : <span>1-50</span> <a href=/list/cs.LG/current?skip=50&show=50>51-100</a> <a href=/list/cs.LG/current?skip=100&show=50>101-150</a> <a href=/list/cs.LG/current?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/current?skip=2450&show=50>2451-2473</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/current?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/current?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/current?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; 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