Machine Learning Nov 2024

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href=/list/cs.LG/2024-11?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2024-11?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2024-11?skip=2300&show=50>2301-2332</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2024-11?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2024-11?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2024-11?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/2411.00074" title="Abstract" id="2411.00074"> arXiv:2411.00074 </a> [<a href="/pdf/2411.00074" title="Download PDF" id="pdf-2411.00074" aria-labelledby="pdf-2411.00074">pdf</a>, <a href="https://arxiv.org/html/2411.00074v1" title="View HTML" id="html-2411.00074" aria-labelledby="html-2411.00074" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00074" title="Other formats" id="oth-2411.00074" aria-labelledby="oth-2411.00074">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> RPS: A Generic Reservoir Patterns Sampler </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Diop,+L">Lamine Diop</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Plantevit,+M">Marc Plantevit</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Soulet,+A">Arnaud Soulet</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at 2024 IEEE International Conference on Big Data </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Combinatorics (math.CO); Probability (math.PR) </div> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2411.00075" title="Abstract" id="2411.00075"> arXiv:2411.00075 </a> [<a href="/pdf/2411.00075" title="Download PDF" id="pdf-2411.00075" aria-labelledby="pdf-2411.00075">pdf</a>, <a href="https://arxiv.org/html/2411.00075v1" title="View HTML" id="html-2411.00075" aria-labelledby="html-2411.00075" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00075" title="Other formats" id="oth-2411.00075" aria-labelledby="oth-2411.00075">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> $\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Haas,+M">Moritz Haas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+J">Jin Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cevher,+V">Volkan Cevher</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vankadara,+L+C">Leena Chennuru Vankadara</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='item3'>[3]</a> <a href ="/abs/2411.00079" title="Abstract" id="2411.00079"> arXiv:2411.00079 </a> [<a href="/pdf/2411.00079" title="Download PDF" id="pdf-2411.00079" aria-labelledby="pdf-2411.00079">pdf</a>, <a href="https://arxiv.org/html/2411.00079v1" title="View HTML" id="html-2411.00079" aria-labelledby="html-2411.00079" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00079" title="Other formats" id="oth-2411.00079" aria-labelledby="oth-2411.00079">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Label Noise: Ignorance Is Bliss </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhu,+Y">Yilun Zhu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+J">Jianxin Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gangrade,+A">Aditya Gangrade</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Scott,+C">Clayton Scott</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='item4'>[4]</a> <a href ="/abs/2411.00110" title="Abstract" id="2411.00110"> arXiv:2411.00110 </a> [<a href="/pdf/2411.00110" title="Download PDF" id="pdf-2411.00110" aria-labelledby="pdf-2411.00110">pdf</a>, <a href="https://arxiv.org/html/2411.00110v1" title="View HTML" id="html-2411.00110" aria-labelledby="html-2411.00110" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00110" title="Other formats" id="oth-2411.00110" aria-labelledby="oth-2411.00110">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Lagrangian neural networks for nonholonomic mechanics </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Diaz,+V+A">Viviana Alejandra Diaz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Salomone,+L+M">Leandro Martin Salomone</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zuccalli,+M">Marcela Zuccalli</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Disordered Systems and Neural Networks (cond-mat.dis-nn); Emerging Technologies (cs.ET); Neural and Evolutionary Computing (cs.NE) </div> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2411.00112" title="Abstract" id="2411.00112"> arXiv:2411.00112 </a> [<a href="/pdf/2411.00112" title="Download PDF" id="pdf-2411.00112" aria-labelledby="pdf-2411.00112">pdf</a>, <a href="https://arxiv.org/html/2411.00112v1" title="View HTML" id="html-2411.00112" aria-labelledby="html-2411.00112" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00112" title="Other formats" id="oth-2411.00112" aria-labelledby="oth-2411.00112">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Derivative-Free Optimization via Finite Difference Approximation: An Experimental Study </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Du-Yi,+W">Wang Du-Yi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guo,+L">Liang Guo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guangwu,+L">Liu Guangwu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kun,+Z">Zhang Kun</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/2411.00126" title="Abstract" id="2411.00126"> arXiv:2411.00126 </a> [<a href="/pdf/2411.00126" title="Download PDF" id="pdf-2411.00126" aria-labelledby="pdf-2411.00126">pdf</a>, <a href="https://arxiv.org/html/2411.00126v1" title="View HTML" id="html-2411.00126" aria-labelledby="html-2411.00126" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00126" title="Other formats" id="oth-2411.00126" aria-labelledby="oth-2411.00126">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Training and Evaluating Causal Forecasting Models for Time-Series </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Crasson,+T">Thomas Crasson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nabet,+Y">Yacine Nabet</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=L%C3%A9cuyer,+M">Mathias L茅cuyer</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/2411.00132" title="Abstract" id="2411.00132"> arXiv:2411.00132 </a> [<a href="/pdf/2411.00132" title="Download PDF" id="pdf-2411.00132" aria-labelledby="pdf-2411.00132">pdf</a>, <a href="/format/2411.00132" title="Other formats" id="oth-2411.00132" aria-labelledby="oth-2411.00132">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+T">Tang Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+M">Mengmeng Ma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Peng,+X">Xi Peng</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> In Proceedings of the 38th Conference on Neural Information Processing Systems (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); Computer Vision and Pattern Recognition (cs.CV) </div> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2411.00136" title="Abstract" id="2411.00136"> arXiv:2411.00136 </a> [<a href="/pdf/2411.00136" title="Download PDF" id="pdf-2411.00136" aria-labelledby="pdf-2411.00136">pdf</a>, <a href="https://arxiv.org/html/2411.00136v1" title="View HTML" id="html-2411.00136" aria-labelledby="html-2411.00136" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00136" title="Other formats" id="oth-2411.00136" aria-labelledby="oth-2411.00136">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chitty-Venkata,+K+T">Krishna Teja Chitty-Venkata</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Raskar,+S">Siddhisanket Raskar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kale,+B">Bharat Kale</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ferdaus,+F">Farah Ferdaus</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tanikanti,+A">Aditya Tanikanti</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Raffenetti,+K">Ken Raffenetti</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Taylor,+V">Valerie Taylor</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Emani,+M">Murali Emani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vishwanath,+V">Venkatram Vishwanath</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='item9'>[9]</a> <a href ="/abs/2411.00139" title="Abstract" id="2411.00139"> arXiv:2411.00139 </a> [<a href="/pdf/2411.00139" title="Download PDF" id="pdf-2411.00139" aria-labelledby="pdf-2411.00139">pdf</a>, <a href="https://arxiv.org/html/2411.00139v1" title="View HTML" id="html-2411.00139" aria-labelledby="html-2411.00139" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00139" title="Other formats" id="oth-2411.00139" aria-labelledby="oth-2411.00139">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning local discrete features in explainable-by-design convolutional neural networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kaplanoglou,+P+I">Pantelis I. Kaplanoglou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Diamantaras,+K">Konstantinos Diamantaras</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2411.00147" title="Abstract" id="2411.00147"> arXiv:2411.00147 </a> [<a href="/pdf/2411.00147" title="Download PDF" id="pdf-2411.00147" aria-labelledby="pdf-2411.00147">pdf</a>, <a href="https://arxiv.org/html/2411.00147v1" title="View HTML" id="html-2411.00147" aria-labelledby="html-2411.00147" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00147" title="Other formats" id="oth-2411.00147" aria-labelledby="oth-2411.00147">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Mutual Information Preserving Neural Network Pruning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Westphal,+C">Charles Westphal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hailes,+S">Stephen Hailes</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Musolesi,+M">Mirco Musolesi</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='item11'>[11]</a> <a href ="/abs/2411.00163" title="Abstract" id="2411.00163"> arXiv:2411.00163 </a> [<a href="/pdf/2411.00163" title="Download PDF" id="pdf-2411.00163" aria-labelledby="pdf-2411.00163">pdf</a>, <a href="https://arxiv.org/html/2411.00163v1" title="View HTML" id="html-2411.00163" aria-labelledby="html-2411.00163" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00163" title="Other formats" id="oth-2411.00163" aria-labelledby="oth-2411.00163">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+W">Weiqin Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+J">Jiawei Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xin,+X">Xin Xin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+S">Sheng Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+B">Binbin Hu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Feng,+Y">Yan Feng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+C">Chun Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+C">Can 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); Information Retrieval (cs.IR) </div> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2411.00171" title="Abstract" id="2411.00171"> arXiv:2411.00171 </a> [<a href="/pdf/2411.00171" title="Download PDF" id="pdf-2411.00171" aria-labelledby="pdf-2411.00171">pdf</a>, <a href="https://arxiv.org/html/2411.00171v1" title="View HTML" id="html-2411.00171" aria-labelledby="html-2411.00171" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00171" title="Other formats" id="oth-2411.00171" aria-labelledby="oth-2411.00171">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cheon,+M">Mujin Cheon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+J+H">Jay H. Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Koh,+D">Dong-Yeun Koh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tsay,+C">Calvin Tsay</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>; Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2411.00179" title="Abstract" id="2411.00179"> arXiv:2411.00179 </a> [<a href="/pdf/2411.00179" title="Download PDF" id="pdf-2411.00179" aria-labelledby="pdf-2411.00179">pdf</a>, <a href="https://arxiv.org/html/2411.00179v1" title="View HTML" id="html-2411.00179" aria-labelledby="html-2411.00179" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00179" title="Other formats" id="oth-2411.00179" aria-labelledby="oth-2411.00179">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> What Makes An Expert? Reviewing How ML Researchers Define "Expert" </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=D%C3%ADaz,+M">Mark D铆az</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Smith,+A+D">Angela DR Smith</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/2411.00180" title="Abstract" id="2411.00180"> arXiv:2411.00180 </a> [<a href="/pdf/2411.00180" title="Download PDF" id="pdf-2411.00180" aria-labelledby="pdf-2411.00180">pdf</a>, <a href="https://arxiv.org/html/2411.00180v1" title="View HTML" id="html-2411.00180" aria-labelledby="html-2411.00180" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00180" title="Other formats" id="oth-2411.00180" aria-labelledby="oth-2411.00180">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Koehler,+F">Felix Koehler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Niedermayr,+S">Simon Niedermayr</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Westermann,+R">R眉diger Westermann</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Thuerey,+N">Nils Thuerey</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at Neurips 2024. The code is available at <a href="https://github.com/tum-pbs/apebench" rel="external noopener nofollow" class="link-external link-https">this https URL</a> and APEBench can be installed via "pip install apebench" </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='item15'>[15]</a> <a href ="/abs/2411.00186" title="Abstract" id="2411.00186"> arXiv:2411.00186 </a> [<a href="/pdf/2411.00186" title="Download PDF" id="pdf-2411.00186" aria-labelledby="pdf-2411.00186">pdf</a>, <a href="/format/2411.00186" title="Other formats" id="oth-2411.00186" aria-labelledby="oth-2411.00186">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Rauba,+P">Paulius Rauba</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Seedat,+N">Nabeel Seedat</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kacprzyk,+K">Krzysztof Kacprzyk</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=van+der+Schaar,+M">Mihaela van der Schaar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Advances in Neural Information Processing Systems 38 (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/2411.00190" title="Abstract" id="2411.00190"> arXiv:2411.00190 </a> [<a href="/pdf/2411.00190" title="Download PDF" id="pdf-2411.00190" aria-labelledby="pdf-2411.00190">pdf</a>, <a href="https://arxiv.org/html/2411.00190v2" title="View HTML" id="html-2411.00190" aria-labelledby="html-2411.00190" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00190" title="Other formats" id="oth-2411.00190" aria-labelledby="oth-2411.00190">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Monitoring fairness in machine learning models that predict patient mortality in the ICU </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=van+Schaik,+T+A">Tempest A. van Schaik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+X">Xinggang Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Atallah,+L">Louis Atallah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Badawi,+O">Omar Badawi</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 8 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2411.00198" title="Abstract" id="2411.00198"> arXiv:2411.00198 </a> [<a href="/pdf/2411.00198" title="Download PDF" id="pdf-2411.00198" aria-labelledby="pdf-2411.00198">pdf</a>, <a href="https://arxiv.org/html/2411.00198v1" title="View HTML" id="html-2411.00198" aria-labelledby="html-2411.00198" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00198" title="Other formats" id="oth-2411.00198" aria-labelledby="oth-2411.00198">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Kernel Operator-Theoretic Bayesian Filter for Nonlinear Dynamical Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+K">Kan Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pr%C3%ADncipe,+J+C">Jos茅 C. Pr铆ncipe</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Signal Processing (eess.SP); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2411.00200" title="Abstract" id="2411.00200"> arXiv:2411.00200 </a> [<a href="/pdf/2411.00200" title="Download PDF" id="pdf-2411.00200" aria-labelledby="pdf-2411.00200">pdf</a>, <a href="https://arxiv.org/html/2411.00200v1" title="View HTML" id="html-2411.00200" aria-labelledby="html-2411.00200" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00200" title="Other formats" id="oth-2411.00200" aria-labelledby="oth-2411.00200">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Oufattole,+N">Nassim Oufattole</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bergamaschi,+T">Teya Bergamaschi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kolo,+A">Aleksia Kolo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jeong,+H">Hyewon Jeong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gaggin,+H">Hanna Gaggin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stultz,+C+M">Collin M. Stultz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=McDermott,+M+B">Matthew B.A. McDermott</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='item19'>[19]</a> <a href ="/abs/2411.00205" title="Abstract" id="2411.00205"> arXiv:2411.00205 </a> [<a href="/pdf/2411.00205" title="Download PDF" id="pdf-2411.00205" aria-labelledby="pdf-2411.00205">pdf</a>, <a href="https://arxiv.org/html/2411.00205v1" title="View HTML" id="html-2411.00205" aria-labelledby="html-2411.00205" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00205" title="Other formats" id="oth-2411.00205" aria-labelledby="oth-2411.00205">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yalcinkaya,+B">Beyazit Yalcinkaya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lauffer,+N">Niklas Lauffer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vazquez-Chanlatte,+M">Marcell Vazquez-Chanlatte</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Seshia,+S+A">Sanjit A. Seshia</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); Formal Languages and Automata Theory (cs.FL) </div> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2411.00233" title="Abstract" id="2411.00233"> arXiv:2411.00233 </a> [<a href="/pdf/2411.00233" title="Download PDF" id="pdf-2411.00233" aria-labelledby="pdf-2411.00233">pdf</a>, <a href="https://arxiv.org/html/2411.00233v1" title="View HTML" id="html-2411.00233" aria-labelledby="html-2411.00233" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00233" title="Other formats" id="oth-2411.00233" aria-labelledby="oth-2411.00233">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> SambaMixer: State of Health Prediction of Li-ion Batteries using Mamba State Space Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Olalde-Verano,+J+I">Jos茅 Ignacio Olalde-Verano</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kirch,+S">Sascha Kirch</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=P%C3%A9rez-Molina,+C">Clara P茅rez-Molina</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Martin,+S">Sergio Martin</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2411.00247" title="Abstract" id="2411.00247"> arXiv:2411.00247 </a> [<a href="/pdf/2411.00247" title="Download PDF" id="pdf-2411.00247" aria-labelledby="pdf-2411.00247">pdf</a>, <a href="https://arxiv.org/html/2411.00247v1" title="View HTML" id="html-2411.00247" aria-labelledby="html-2411.00247" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00247" title="Other formats" id="oth-2411.00247" aria-labelledby="oth-2411.00247">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Jeffares,+A">Alan Jeffares</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Curth,+A">Alicia Curth</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=van+der+Schaar,+M">Mihaela van der Schaar</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at Conference on Neural Information Processing Systems (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); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2411.00259" title="Abstract" id="2411.00259"> arXiv:2411.00259 </a> [<a href="/pdf/2411.00259" title="Download PDF" id="pdf-2411.00259" aria-labelledby="pdf-2411.00259">pdf</a>, <a href="https://arxiv.org/html/2411.00259v1" title="View HTML" id="html-2411.00259" aria-labelledby="html-2411.00259" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00259" title="Other formats" id="oth-2411.00259" aria-labelledby="oth-2411.00259">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Smerkous,+D">David Smerkous</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bai,+Q">Qinxun Bai</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+F">Fuxin Li</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> </div> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2411.00263" title="Abstract" id="2411.00263"> arXiv:2411.00263 </a> [<a href="/pdf/2411.00263" title="Download PDF" id="pdf-2411.00263" aria-labelledby="pdf-2411.00263">pdf</a>, <a href="https://arxiv.org/html/2411.00263v1" title="View HTML" id="html-2411.00263" aria-labelledby="html-2411.00263" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00263" title="Other formats" id="oth-2411.00263" aria-labelledby="oth-2411.00263">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+G">Grace Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Powell,+L">Luca Powell</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Svoboda,+F">Filip Svoboda</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lane,+N">Nicholas Lane</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2411.00265" title="Abstract" id="2411.00265"> arXiv:2411.00265 </a> [<a href="/pdf/2411.00265" title="Download PDF" id="pdf-2411.00265" aria-labelledby="pdf-2411.00265">pdf</a>, <a href="https://arxiv.org/html/2411.00265v1" title="View HTML" id="html-2411.00265" aria-labelledby="html-2411.00265" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00265" title="Other formats" id="oth-2411.00265" aria-labelledby="oth-2411.00265">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Quantifying calibration error in modern neural networks through evidence based theory </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ouattara,+K+I">Koffi Ismael Ouattara</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Logic (math.LO) </div> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2411.00266" title="Abstract" id="2411.00266"> arXiv:2411.00266 </a> [<a href="/pdf/2411.00266" title="Download PDF" id="pdf-2411.00266" aria-labelledby="pdf-2411.00266">pdf</a>, <a href="/format/2411.00266" title="Other formats" id="oth-2411.00266" aria-labelledby="oth-2411.00266">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Systematic Review of NeurIPS Dataset Management Practices </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+Y">Yiwei Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ajmani,+L">Leah Ajmani</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Longpre,+S">Shayne Longpre</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+H">Hanlin Li</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 10 pages, 2 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2411.00268" title="Abstract" id="2411.00268"> arXiv:2411.00268 </a> [<a href="/pdf/2411.00268" title="Download PDF" id="pdf-2411.00268" aria-labelledby="pdf-2411.00268">pdf</a>, <a href="/format/2411.00268" title="Other formats" id="oth-2411.00268" aria-labelledby="oth-2411.00268">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Clustering ensemble algorithm with high-order consistency learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gan,+J">Jianwen Gan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Y">Yan Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+P">Peng Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+L">Liang Du</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> in Chinese language </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Journal of Computer Applications, 2023, 43(9),2665-2672 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2411.00270" title="Abstract" id="2411.00270"> arXiv:2411.00270 </a> [<a href="/pdf/2411.00270" title="Download PDF" id="pdf-2411.00270" aria-labelledby="pdf-2411.00270">pdf</a>, <a href="/format/2411.00270" title="Other formats" id="oth-2411.00270" aria-labelledby="oth-2411.00270">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Unsupervised Feature Selection Algorithm Based on Graph Filtering and Self-representation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Liang,+Y">Yunhui Liang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gan,+J">Jianwen Gan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+Y">Yan Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+P">Peng Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+L">Liang Du</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> in Chinese language </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Journal of Jilin University(Science Edition),2024,62(03),655-664 </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/2411.00273" title="Abstract" id="2411.00273"> arXiv:2411.00273 </a> [<a href="/pdf/2411.00273" title="Download PDF" id="pdf-2411.00273" aria-labelledby="pdf-2411.00273">pdf</a>, <a href="https://arxiv.org/html/2411.00273v1" title="View HTML" id="html-2411.00273" aria-labelledby="html-2411.00273" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00273" title="Other formats" id="oth-2411.00273" aria-labelledby="oth-2411.00273">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Efficient Model Compression for Bayesian Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Saha,+D">Diptarka Saha</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Z">Zihe Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liang,+F">Feng Liang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Applications (stat.AP); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2411.00278" title="Abstract" id="2411.00278"> arXiv:2411.00278 </a> [<a href="/pdf/2411.00278" title="Download PDF" id="pdf-2411.00278" aria-labelledby="pdf-2411.00278">pdf</a>, <a href="/format/2411.00278" title="Other formats" id="oth-2411.00278" aria-labelledby="oth-2411.00278">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+Q">Quan Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pei,+C">Changhua Pei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+F">Fei Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Han,+J">Jing Han</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+Z">Zhengwei Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pei,+D">Dan Pei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+H">Haiming Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xie,+G">Gaogang Xie</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+J">Jianhui Li</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/2411.00282" title="Abstract" id="2411.00282"> arXiv:2411.00282 </a> [<a href="/pdf/2411.00282" title="Download PDF" id="pdf-2411.00282" aria-labelledby="pdf-2411.00282">pdf</a>, <a href="/format/2411.00282" title="Other formats" id="oth-2411.00282" aria-labelledby="oth-2411.00282">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cismaru,+A+T">Alexandru T. Cismaru</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 5 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='item31'>[31]</a> <a href ="/abs/2411.00287" title="Abstract" id="2411.00287"> arXiv:2411.00287 </a> [<a href="/pdf/2411.00287" title="Download PDF" id="pdf-2411.00287" aria-labelledby="pdf-2411.00287">pdf</a>, <a href="/format/2411.00287" title="Other formats" id="oth-2411.00287" aria-labelledby="oth-2411.00287">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Golgoon,+A">Ashkan Golgoon</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Franks,+R">Ryan Franks</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Filom,+K">Khashayar Filom</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kannan,+A+R">Arjun Ravi Kannan</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); Numerical Analysis (math.NA); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/2411.00288" title="Abstract" id="2411.00288"> arXiv:2411.00288 </a> [<a href="/pdf/2411.00288" title="Download PDF" id="pdf-2411.00288" aria-labelledby="pdf-2411.00288">pdf</a>, <a href="https://arxiv.org/html/2411.00288v1" title="View HTML" id="html-2411.00288" aria-labelledby="html-2411.00288" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00288" title="Other formats" id="oth-2411.00288" aria-labelledby="oth-2411.00288">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Inducing Semi-Structured Sparsity by Masking for Efficient Model Inference in Convolutional Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Danhofer,+D+A">David A. Danhofer</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 15 pages, 3 figures; this work will be presented at the NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles and Scalability (FITML) </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); Neural and Evolutionary Computing (cs.NE); Performance (cs.PF) </div> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2411.00311" title="Abstract" id="2411.00311"> arXiv:2411.00311 </a> [<a href="/pdf/2411.00311" title="Download PDF" id="pdf-2411.00311" aria-labelledby="pdf-2411.00311">pdf</a>, <a href="https://arxiv.org/html/2411.00311v1" title="View HTML" id="html-2411.00311" aria-labelledby="html-2411.00311" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00311" title="Other formats" id="oth-2411.00311" aria-labelledby="oth-2411.00311">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+Y">Yeachan Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+J">Junho Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mok,+W">Wing-Lam Mok</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Park,+J">Jun-Hyung Park</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+S">SangKeun Lee</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Published at Findings of ACL 2023 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) </div> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2411.00322" title="Abstract" id="2411.00322"> arXiv:2411.00322 </a> [<a href="/pdf/2411.00322" title="Download PDF" id="pdf-2411.00322" aria-labelledby="pdf-2411.00322">pdf</a>, <a href="https://arxiv.org/html/2411.00322v1" title="View HTML" id="html-2411.00322" aria-labelledby="html-2411.00322" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00322" title="Other formats" id="oth-2411.00322" aria-labelledby="oth-2411.00322">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Constant Acceleration Flow </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Park,+D">Dogyun Park</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+S">Sojin Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+S">Sihyeon Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+T">Taehoon Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hong,+Y">Youngjoon Hong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+H+J">Hyunwoo J. Kim</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='item35'>[35]</a> <a href ="/abs/2411.00329" title="Abstract" id="2411.00329"> arXiv:2411.00329 </a> [<a href="/pdf/2411.00329" title="Download PDF" id="pdf-2411.00329" aria-labelledby="pdf-2411.00329">pdf</a>, <a href="https://arxiv.org/html/2411.00329v1" title="View HTML" id="html-2411.00329" aria-labelledby="html-2411.00329" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00329" title="Other formats" id="oth-2411.00329" aria-labelledby="oth-2411.00329">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Personalized Federated Learning via Feature Distribution Adaptation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mclaughlin,+C+J">Connor J. Mclaughlin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Su,+L">Lili Su</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 38th Annual Conference on Neural Information Processing Systems (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='item36'>[36]</a> <a href ="/abs/2411.00336" title="Abstract" id="2411.00336"> arXiv:2411.00336 </a> [<a href="/pdf/2411.00336" title="Download PDF" id="pdf-2411.00336" aria-labelledby="pdf-2411.00336">pdf</a>, <a href="https://arxiv.org/html/2411.00336v1" title="View HTML" id="html-2411.00336" aria-labelledby="html-2411.00336" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00336" title="Other formats" id="oth-2411.00336" aria-labelledby="oth-2411.00336">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> StepCountJITAI: simulation environment for RL with application to physical activity adaptive intervention </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Karine,+K">Karine Karine</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Marlin,+B+M">Benjamin M. Marlin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at NeurIPS 2024 workshop on Behavioral ML </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='item37'>[37]</a> <a href ="/abs/2411.00359" title="Abstract" id="2411.00359"> arXiv:2411.00359 </a> [<a href="/pdf/2411.00359" title="Download PDF" id="pdf-2411.00359" aria-labelledby="pdf-2411.00359">pdf</a>, <a href="https://arxiv.org/html/2411.00359v1" title="View HTML" id="html-2411.00359" aria-labelledby="html-2411.00359" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00359" title="Other formats" id="oth-2411.00359" aria-labelledby="oth-2411.00359">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Constrained Diffusion Implicit Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Jayaram,+V">Vivek Jayaram</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kemelmacher-Shlizerman,+I">Ira Kemelmacher-Shlizerman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Seitz,+S+M">Steven M. Seitz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Thickstun,+J">John Thickstun</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Image and Video Processing (eess.IV) </div> </div> </dd> <dt> <a name='item38'>[38]</a> <a href ="/abs/2411.00360" title="Abstract" id="2411.00360"> arXiv:2411.00360 </a> [<a href="/pdf/2411.00360" title="Download PDF" id="pdf-2411.00360" aria-labelledby="pdf-2411.00360">pdf</a>, <a href="https://arxiv.org/html/2411.00360v1" title="View HTML" id="html-2411.00360" aria-labelledby="html-2411.00360" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00360" title="Other formats" id="oth-2411.00360" aria-labelledby="oth-2411.00360">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Jung,+Y">Yeonsung Jung</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Song,+J">Jaeyun Song</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+J+Y">June Yong Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+J">Jin-Hwa Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kim,+S">Sung-Yub Kim</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+E">Eunho Yang</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/2411.00361" title="Abstract" id="2411.00361"> arXiv:2411.00361 </a> [<a href="/pdf/2411.00361" title="Download PDF" id="pdf-2411.00361" aria-labelledby="pdf-2411.00361">pdf</a>, <a href="https://arxiv.org/html/2411.00361v1" title="View HTML" id="html-2411.00361" aria-labelledby="html-2411.00361" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00361" title="Other formats" id="oth-2411.00361" aria-labelledby="oth-2411.00361">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Hierarchical Preference Optimization: Learning to achieve goals via feasible subgoals prediction </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Singh,+U">Utsav Singh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chakraborty,+S">Souradip Chakraborty</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Suttle,+W+A">Wesley A. Suttle</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sadler,+B+M">Brian M. Sadler</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sahu,+A+K">Anit Kumar Sahu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shah,+M">Mubarak Shah</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Namboodiri,+V+P">Vinay P. Namboodiri</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bedi,+A+S">Amrit Singh Bedi</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='item40'>[40]</a> <a href ="/abs/2411.00365" title="Abstract" id="2411.00365"> arXiv:2411.00365 </a> [<a href="/pdf/2411.00365" title="Download PDF" id="pdf-2411.00365" aria-labelledby="pdf-2411.00365">pdf</a>, <a href="/format/2411.00365" title="Other formats" id="oth-2411.00365" aria-labelledby="oth-2411.00365">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ROSS:RObust decentralized Stochastic learning based on Shapley values </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+L">Lina Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yuan,+Y">Yunsheng Yuan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+F">Feng Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Duan,+L">Lingjie Duan</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='item41'>[41]</a> <a href ="/abs/2411.00372" title="Abstract" id="2411.00372"> arXiv:2411.00372 </a> [<a href="/pdf/2411.00372" title="Download PDF" id="pdf-2411.00372" aria-labelledby="pdf-2411.00372">pdf</a>, <a href="https://arxiv.org/html/2411.00372v1" title="View HTML" id="html-2411.00372" aria-labelledby="html-2411.00372" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00372" title="Other formats" id="oth-2411.00372" aria-labelledby="oth-2411.00372">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Generalizability of Memorization Neural Networks </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+L">Lijia Yu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gao,+X">Xiao-Shan Gao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+L">Lijun Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Miao,+Y">Yibo Miao</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='item42'>[42]</a> <a href ="/abs/2411.00382" title="Abstract" id="2411.00382"> arXiv:2411.00382 </a> [<a href="/pdf/2411.00382" title="Download PDF" id="pdf-2411.00382" aria-labelledby="pdf-2411.00382">pdf</a>, <a href="https://arxiv.org/html/2411.00382v1" title="View HTML" id="html-2411.00382" aria-labelledby="html-2411.00382" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00382" title="Other formats" id="oth-2411.00382" aria-labelledby="oth-2411.00382">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Communication Learning in Multi-Agent Systems from Graph Modeling Perspective </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+S">Shengchao Hu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shen,+L">Li Shen</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=Tao,+D">Dacheng Tao</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Extension of the corresponding ICLR edition: <a href="https://arxiv.org/abs/2405.08550" data-arxiv-id="2405.08550" class="link-https">arXiv:2405.08550</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Multiagent Systems (cs.MA) </div> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2411.00383" title="Abstract" id="2411.00383"> arXiv:2411.00383 </a> [<a href="/pdf/2411.00383" title="Download PDF" id="pdf-2411.00383" aria-labelledby="pdf-2411.00383">pdf</a>, <a href="https://arxiv.org/html/2411.00383v1" title="View HTML" id="html-2411.00383" aria-labelledby="html-2411.00383" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00383" title="Other formats" id="oth-2411.00383" aria-labelledby="oth-2411.00383">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=He,+J">Junlin He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+J">Jinxiao Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+S">Susu Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+W">Wei Ma</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted by NeurIPS 2024 as a poster </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='item44'>[44]</a> <a href ="/abs/2411.00392" title="Abstract" id="2411.00392"> arXiv:2411.00392 </a> [<a href="/pdf/2411.00392" title="Download PDF" id="pdf-2411.00392" aria-labelledby="pdf-2411.00392">pdf</a>, <a href="https://arxiv.org/html/2411.00392v1" title="View HTML" id="html-2411.00392" aria-labelledby="html-2411.00392" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00392" title="Other formats" id="oth-2411.00392" aria-labelledby="oth-2411.00392">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=He,+J">Junlin He</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+J">Jinxiao Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ma,+W">Wei Ma</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> accepted by NeurIPS 2024 as a poster </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2411.00393" title="Abstract" id="2411.00393"> arXiv:2411.00393 </a> [<a href="/pdf/2411.00393" title="Download PDF" id="pdf-2411.00393" aria-labelledby="pdf-2411.00393">pdf</a>, <a href="https://arxiv.org/html/2411.00393v4" title="View HTML" id="html-2411.00393" aria-labelledby="html-2411.00393" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00393" title="Other formats" id="oth-2411.00393" aria-labelledby="oth-2411.00393">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Advantages of Neural Population Coding for Deep Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Hoffmann,+H">Heiko Hoffmann</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='item46'>[46]</a> <a href ="/abs/2411.00401" title="Abstract" id="2411.00401"> arXiv:2411.00401 </a> [<a href="/pdf/2411.00401" title="Download PDF" id="pdf-2411.00401" aria-labelledby="pdf-2411.00401">pdf</a>, <a href="/format/2411.00401" title="Other formats" id="oth-2411.00401" aria-labelledby="oth-2411.00401">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Z">Zhi Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chow,+C">Chris Chow</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Yasi Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+Y">Yanchao Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+H">Haochen Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jiang,+E+H">Eric Hanchen Jiang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+H">Han Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+F">Furong Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cui,+Y">Yuchen Cui</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Padilla,+O+H+M">Oscar Hernan Madrid Padilla</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI) </div> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/2411.00404" title="Abstract" id="2411.00404"> arXiv:2411.00404 </a> [<a href="/pdf/2411.00404" title="Download PDF" id="pdf-2411.00404" aria-labelledby="pdf-2411.00404">pdf</a>, <a href="https://arxiv.org/html/2411.00404v1" title="View HTML" id="html-2411.00404" aria-labelledby="html-2411.00404" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00404" title="Other formats" id="oth-2411.00404" aria-labelledby="oth-2411.00404">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fast Adaptation with Kernel and Gradient based Meta Leaning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Park,+J">JuneYoung Park</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kang,+M">MinJae Kang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages(with reference), 2 figures, 4 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item48'>[48]</a> <a href ="/abs/2411.00406" title="Abstract" id="2411.00406"> arXiv:2411.00406 </a> [<a href="/pdf/2411.00406" title="Download PDF" id="pdf-2411.00406" aria-labelledby="pdf-2411.00406">pdf</a>, <a href="https://arxiv.org/html/2411.00406v1" title="View HTML" id="html-2411.00406" aria-labelledby="html-2411.00406" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00406" title="Other formats" id="oth-2411.00406" aria-labelledby="oth-2411.00406">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MoD: A Distribution-Based Approach for Merging Large Language Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Dang,+Q">Quy-Anh Dang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ngo,+C">Chris Ngo</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span> </div> </div> </dd> <dt> <a name='item49'>[49]</a> <a href ="/abs/2411.00409" title="Abstract" id="2411.00409"> arXiv:2411.00409 </a> [<a href="/pdf/2411.00409" title="Download PDF" id="pdf-2411.00409" aria-labelledby="pdf-2411.00409">pdf</a>, <a href="https://arxiv.org/html/2411.00409v1" title="View HTML" id="html-2411.00409" aria-labelledby="html-2411.00409" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00409" title="Other formats" id="oth-2411.00409" aria-labelledby="oth-2411.00409">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Black-Box Forgetting </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kuwana,+Y">Yusuke Kuwana</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Goto,+Y">Yuta Goto</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shibata,+T">Takashi Shibata</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Irie,+G">Go Irie</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> </div> </div> </dd> <dt> <a name='item50'>[50]</a> <a href ="/abs/2411.00412" title="Abstract" id="2411.00412"> arXiv:2411.00412 </a> [<a href="/pdf/2411.00412" title="Download PDF" id="pdf-2411.00412" aria-labelledby="pdf-2411.00412">pdf</a>, <a href="/format/2411.00412" title="Other formats" id="oth-2411.00412" aria-labelledby="oth-2411.00412">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lyu,+B">Bohan Lyu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Cao,+Y">Yadi Cao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Watson-Parris,+D">Duncan Watson-Parris</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bergen,+L">Leon Bergen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Berg-Kirkpatrick,+T">Taylor Berg-Kirkpatrick</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yu,+R">Rose Yu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 26 pages, 15 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) </div> </div> </dd> </dl> <div class='paging'>Total of 2332 entries : <span>1-50</span> <a href=/list/cs.LG/2024-11?skip=50&show=50>51-100</a> <a href=/list/cs.LG/2024-11?skip=100&show=50>101-150</a> <a href=/list/cs.LG/2024-11?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/cs.LG/2024-11?skip=2300&show=50>2301-2332</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/cs.LG/2024-11?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/cs.LG/2024-11?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/cs.LG/2024-11?skip=0&show=2000 rel="nofollow"> all</a> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">  <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; 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