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Statistics Nov 2024
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class='morefewer'>Showing up to 50 entries per page: <a href=/list/stat/2024-11?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/stat/2024-11?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/stat/2024-11?skip=0&show=2000 rel="nofollow"> all</a> </div> <dl id='articles'> <dt> <a name='item1'>[1]</a> <a href ="/abs/2411.00011" title="Abstract" id="2411.00011"> arXiv:2411.00011 </a> [<a href="/pdf/2411.00011" title="Download PDF" id="pdf-2411.00011" aria-labelledby="pdf-2411.00011">pdf</a>, <a href="https://arxiv.org/html/2411.00011v2" title="View HTML" id="html-2411.00011" aria-labelledby="html-2411.00011" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00011" title="Other formats" id="oth-2411.00011" aria-labelledby="oth-2411.00011">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Solving the 2D Advection-Diffusion Equation using Fixed-Depth Symbolic Regression and Symbolic Differentiation without Expression Trees </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Finkelstein,+E">Edward Finkelstein</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 22 equations. Improved results added. Figures removed for brevity. Typos corrected. The text was made more concise and up-to-date in some areas </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computation (stat.CO)</span>; Machine Learning (cs.LG); Numerical Analysis (math.NA) </div> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2411.00109" title="Abstract" id="2411.00109"> arXiv:2411.00109 </a> [<a href="/pdf/2411.00109" title="Download PDF" id="pdf-2411.00109" aria-labelledby="pdf-2411.00109">pdf</a>, <a href="https://arxiv.org/html/2411.00109v2" title="View HTML" id="html-2411.00109" aria-labelledby="html-2411.00109" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00109" title="Other formats" id="oth-2411.00109" aria-labelledby="oth-2411.00109">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Prospective Learning: Learning for a Dynamic Future </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=De+Silva,+A">Ashwin De Silva</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Ramesh,+R">Rahul Ramesh</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Yang,+R">Rubing Yang</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Yu,+S">Siyu Yu</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Vogelstein,+J+T">Joshua T Vogelstein</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Chaudhari,+P">Pratik Chaudhari</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to NeurIPS 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2411.00113" title="Abstract" id="2411.00113"> arXiv:2411.00113 </a> [<a href="/pdf/2411.00113" title="Download PDF" id="pdf-2411.00113" aria-labelledby="pdf-2411.00113">pdf</a>, <a href="https://arxiv.org/html/2411.00113v2" title="View HTML" id="html-2411.00113" aria-labelledby="html-2411.00113" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00113" title="Other formats" id="oth-2411.00113" aria-labelledby="oth-2411.00113">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Geometric Framework for Understanding Memorization in Generative Models </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Ross,+B+L">Brendan Leigh Ross</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Kamkari,+H">Hamidreza Kamkari</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Wu,+T">Tongzi Wu</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Hosseinzadeh,+R">Rasa Hosseinzadeh</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Liu,+Z">Zhaoyan Liu</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Stein,+G">George Stein</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Cresswell,+J+C">Jesse C. Cresswell</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Loaiza-Ganem,+G">Gabriel Loaiza-Ganem</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to ICLR 2025 (Spotlight) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2411.00161" title="Abstract" id="2411.00161"> arXiv:2411.00161 </a> [<a href="/pdf/2411.00161" title="Download PDF" id="pdf-2411.00161" aria-labelledby="pdf-2411.00161">pdf</a>, <a href="/format/2411.00161" title="Other formats" id="oth-2411.00161" aria-labelledby="oth-2411.00161">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Residual Deep Gaussian Processes on Manifolds </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Wyrwal,+K">Kacper Wyrwal</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Krause,+A">Andreas Krause</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Borovitskiy,+V">Viacheslav Borovitskiy</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2411.00191" title="Abstract" id="2411.00191"> arXiv:2411.00191 </a> [<a href="/pdf/2411.00191" title="Download PDF" id="pdf-2411.00191" aria-labelledby="pdf-2411.00191">pdf</a>, <a href="https://arxiv.org/html/2411.00191v1" title="View HTML" id="html-2411.00191" aria-labelledby="html-2411.00191" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00191" title="Other formats" id="oth-2411.00191" aria-labelledby="oth-2411.00191">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sharp Bounds on the Variance of General Regression Adjustment in Randomized Experiments </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Mikhaeil,+J+M">Jonas M. Mikhaeil</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Green,+D+P">Donald P. Green</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Statistics Theory (math.ST) </div> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2411.00213" title="Abstract" id="2411.00213"> arXiv:2411.00213 </a> [<a href="/pdf/2411.00213" title="Download PDF" id="pdf-2411.00213" aria-labelledby="pdf-2411.00213">pdf</a>, <a href="https://arxiv.org/html/2411.00213v1" title="View HTML" id="html-2411.00213" aria-labelledby="html-2411.00213" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00213" title="Other formats" id="oth-2411.00213" aria-labelledby="oth-2411.00213">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Learning Mixtures of Unknown Causal Interventions </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Kumar,+A">Abhinav Kumar</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Shiragur,+K">Kirankumar Shiragur</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Uhler,+C">Caroline Uhler</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2411.00214" title="Abstract" id="2411.00214"> arXiv:2411.00214 </a> [<a href="/pdf/2411.00214" title="Download PDF" id="pdf-2411.00214" aria-labelledby="pdf-2411.00214">pdf</a>, <a href="https://arxiv.org/html/2411.00214v1" title="View HTML" id="html-2411.00214" aria-labelledby="html-2411.00214" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00214" title="Other formats" id="oth-2411.00214" aria-labelledby="oth-2411.00214">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspective </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Zhu,+J">Jia-Jie Zhu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG); Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2411.00218" title="Abstract" id="2411.00218"> arXiv:2411.00218 </a> [<a href="/pdf/2411.00218" title="Download PDF" id="pdf-2411.00218" aria-labelledby="pdf-2411.00218">pdf</a>, <a href="https://arxiv.org/html/2411.00218v1" title="View HTML" id="html-2411.00218" aria-labelledby="html-2411.00218" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00218" title="Other formats" id="oth-2411.00218" aria-labelledby="oth-2411.00218">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Nudging state-space models for Bayesian filtering under misspecified dynamics </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Gonzalez,+F">Fabian Gonzalez</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Akyildiz,+O+D">O. Deniz Akyildiz</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Crisan,+D">Dan Crisan</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Miguez,+J">Joaquin Miguez</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computation (stat.CO)</span>; Probability (math.PR) </div> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2411.00229" title="Abstract" id="2411.00229"> arXiv:2411.00229 </a> [<a href="/pdf/2411.00229" title="Download PDF" id="pdf-2411.00229" aria-labelledby="pdf-2411.00229">pdf</a>, <a href="/format/2411.00229" title="Other formats" id="oth-2411.00229" aria-labelledby="oth-2411.00229">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Minimum Empirical Divergence for Sub-Gaussian Linear Bandits </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Balagopalan,+K">Kapilan Balagopalan</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Jun,+K">Kwang-Sung Jun</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2411.00253" title="Abstract" id="2411.00253"> arXiv:2411.00253 </a> [<a href="/pdf/2411.00253" title="Download PDF" id="pdf-2411.00253" aria-labelledby="pdf-2411.00253">pdf</a>, <a href="https://arxiv.org/html/2411.00253v1" title="View HTML" id="html-2411.00253" aria-labelledby="html-2411.00253" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00253" title="Other formats" id="oth-2411.00253" aria-labelledby="oth-2411.00253">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Adaptive minimax estimation for discretely observed L茅vy processes </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Duval,+C">C茅line Duval</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Jalal,+T">Taher Jalal</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Mariucci,+E">Ester Mariucci</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 27 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Statistics Theory (math.ST)</span>; Probability (math.PR) </div> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/2411.00256" title="Abstract" id="2411.00256"> arXiv:2411.00256 </a> [<a href="/pdf/2411.00256" title="Download PDF" id="pdf-2411.00256" aria-labelledby="pdf-2411.00256">pdf</a>, <a href="https://arxiv.org/html/2411.00256v1" title="View HTML" id="html-2411.00256" aria-labelledby="html-2411.00256" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00256" title="Other formats" id="oth-2411.00256" aria-labelledby="oth-2411.00256">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Liu,+Z">Zihe Liu</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Saha,+D">Diptarka Saha</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Liang,+F">Feng Liang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span> </div> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2411.00286" title="Abstract" id="2411.00286"> arXiv:2411.00286 </a> [<a href="/pdf/2411.00286" title="Download PDF" id="pdf-2411.00286" aria-labelledby="pdf-2411.00286">pdf</a>, <a href="/format/2411.00286" title="Other formats" id="oth-2411.00286" aria-labelledby="oth-2411.00286">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Sensitivity of Bayesian Kernel Machine Regression (BKMR) to Data Distribution: A Comprehensive Simulation Analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Hasan,+K+T">Kazi Tanvir Hasan</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Odom,+G">Gabriel Odom</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Bursac,+Z">Zoran Bursac</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Ibrahimou,+B">Boubakari Ibrahimou</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This article is submitted to the Journal of Statistical Computation and Simulation and currently under review </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computation (stat.CO)</span>; Applications (stat.AP) </div> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2411.00297" title="Abstract" id="2411.00297"> arXiv:2411.00297 </a> [<a href="/pdf/2411.00297" title="Download PDF" id="pdf-2411.00297" aria-labelledby="pdf-2411.00297">pdf</a>, <a href="https://arxiv.org/html/2411.00297v1" title="View HTML" id="html-2411.00297" aria-labelledby="html-2411.00297" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00297" title="Other formats" id="oth-2411.00297" aria-labelledby="oth-2411.00297">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Analysis of ELSA COVID-19 Substudy response rate using machine learning algorithms </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Qazvini,+M">Marjan Qazvini</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Applications (stat.AP)</span>; Machine Learning (cs.LG); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2411.00317" title="Abstract" id="2411.00317"> arXiv:2411.00317 </a> [<a href="/pdf/2411.00317" title="Download PDF" id="pdf-2411.00317" aria-labelledby="pdf-2411.00317">pdf</a>, <a href="https://arxiv.org/html/2411.00317v1" title="View HTML" id="html-2411.00317" aria-labelledby="html-2411.00317" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00317" title="Other formats" id="oth-2411.00317" aria-labelledby="oth-2411.00317">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Forecasting Mortality in the Middle-Aged and Older Population of England: A 1D-CNN Approach </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Qazvini,+M">Marjan Qazvini</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Applications (stat.AP)</span>; Machine Learning (cs.LG); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2411.00328" title="Abstract" id="2411.00328"> arXiv:2411.00328 </a> [<a href="/pdf/2411.00328" title="Download PDF" id="pdf-2411.00328" aria-labelledby="pdf-2411.00328">pdf</a>, <a href="https://arxiv.org/html/2411.00328v1" title="View HTML" id="html-2411.00328" aria-labelledby="html-2411.00328" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00328" title="Other formats" id="oth-2411.00328" aria-labelledby="oth-2411.00328">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> How many classifiers do we need? </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Kim,+H">Hyunsuk Kim</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Hodgkinson,+L">Liam Hodgkinson</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Theisen,+R">Ryan Theisen</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Mahoney,+M+W">Michael W. Mahoney</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2411.00339" title="Abstract" id="2411.00339"> arXiv:2411.00339 </a> [<a href="/pdf/2411.00339" title="Download PDF" id="pdf-2411.00339" aria-labelledby="pdf-2411.00339">pdf</a>, <a href="/format/2411.00339" title="Other formats" id="oth-2411.00339" aria-labelledby="oth-2411.00339">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Unified theory of upper confidence bound policies for bandit problems targeting total reward, maximal reward, and more </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Kikkawa,+N">Nobuaki Kikkawa</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Ohno,+H">Hiroshi Ohno</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2411.00346" title="Abstract" id="2411.00346"> arXiv:2411.00346 </a> [<a href="/pdf/2411.00346" title="Download PDF" id="pdf-2411.00346" aria-labelledby="pdf-2411.00346">pdf</a>, <a href="https://arxiv.org/html/2411.00346v1" title="View HTML" id="html-2411.00346" aria-labelledby="html-2411.00346" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00346" title="Other formats" id="oth-2411.00346" aria-labelledby="oth-2411.00346">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Estimating Broad Sense Heritability via Kernel Ridge Regression </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Bley,+O">Olivia Bley</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Lei,+E">Elizabeth Lei</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Zhou,+A">Andy Zhou</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Shen,+X">Xiaoxi Shen</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span> </div> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2411.00354" title="Abstract" id="2411.00354"> arXiv:2411.00354 </a> [<a href="/pdf/2411.00354" title="Download PDF" id="pdf-2411.00354" aria-labelledby="pdf-2411.00354">pdf</a>, <a href="https://arxiv.org/html/2411.00354v1" title="View HTML" id="html-2411.00354" aria-labelledby="html-2411.00354" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00354" title="Other formats" id="oth-2411.00354" aria-labelledby="oth-2411.00354">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Classification problem in liability insurance using machine learning models: a comparative study </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Qazvini,+M">Marjan Qazvini</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Applications (stat.AP)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/2411.00371" title="Abstract" id="2411.00371"> arXiv:2411.00371 </a> [<a href="/pdf/2411.00371" title="Download PDF" id="pdf-2411.00371" aria-labelledby="pdf-2411.00371">pdf</a>, <a href="https://arxiv.org/html/2411.00371v1" title="View HTML" id="html-2411.00371" aria-labelledby="html-2411.00371" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00371" title="Other formats" id="oth-2411.00371" aria-labelledby="oth-2411.00371">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Blocked Gibbs Sampling for Improved Convergence in Finite Mixture Models </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Swanson,+D+M">David Michael Swanson</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Statistics Theory (math.ST)</span>; Computation (stat.CO) </div> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2411.00405" title="Abstract" id="2411.00405"> arXiv:2411.00405 </a> [<a href="/pdf/2411.00405" title="Download PDF" id="pdf-2411.00405" aria-labelledby="pdf-2411.00405">pdf</a>, <a href="/format/2411.00405" title="Other formats" id="oth-2411.00405" aria-labelledby="oth-2411.00405">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Nguyen,+T+N">Tuan Ngo Nguyen</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Jun,+K">Kwang-Sung Jun</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2411.00429" title="Abstract" id="2411.00429"> arXiv:2411.00429 </a> [<a href="/pdf/2411.00429" title="Download PDF" id="pdf-2411.00429" aria-labelledby="pdf-2411.00429">pdf</a>, <a href="https://arxiv.org/html/2411.00429v1" title="View HTML" id="html-2411.00429" aria-labelledby="html-2411.00429" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00429" title="Other formats" id="oth-2411.00429" aria-labelledby="oth-2411.00429">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Unbiased mixed variables distance </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=van+de+Velden,+M">Michel van de Velden</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=D'Enza,+A+I">Alfonso Iodice D'Enza</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Markos,+A">Angelos Markos</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Cavicchia,+C">Carlo Cavicchia</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 40 pages, 9 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span> </div> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2411.00471" title="Abstract" id="2411.00471"> arXiv:2411.00471 </a> [<a href="/pdf/2411.00471" title="Download PDF" id="pdf-2411.00471" aria-labelledby="pdf-2411.00471">pdf</a>, <a href="https://arxiv.org/html/2411.00471v1" title="View HTML" id="html-2411.00471" aria-labelledby="html-2411.00471" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00471" title="Other formats" id="oth-2411.00471" aria-labelledby="oth-2411.00471">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Porwal,+A">Anupreet Porwal</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Rodriguez,+A">Abel Rodriguez</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2411.00520" title="Abstract" id="2411.00520"> arXiv:2411.00520 </a> [<a href="/pdf/2411.00520" title="Download PDF" id="pdf-2411.00520" aria-labelledby="pdf-2411.00520">pdf</a>, <a href="https://arxiv.org/html/2411.00520v1" title="View HTML" id="html-2411.00520" aria-labelledby="html-2411.00520" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00520" title="Other formats" id="oth-2411.00520" aria-labelledby="oth-2411.00520">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Calibrated quantile prediction for Growth-at-Risk </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Bogani,+P">Pietro Bogani</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Fontana,+M">Matteo Fontana</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Neri,+L">Luca Neri</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Vantini,+S">Simone Vantini</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Econometrics (econ.EM) </div> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2411.00525" title="Abstract" id="2411.00525"> arXiv:2411.00525 </a> [<a href="/pdf/2411.00525" title="Download PDF" id="pdf-2411.00525" aria-labelledby="pdf-2411.00525">pdf</a>, <a href="https://arxiv.org/html/2411.00525v1" title="View HTML" id="html-2411.00525" aria-labelledby="html-2411.00525" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00525" title="Other formats" id="oth-2411.00525" aria-labelledby="oth-2411.00525">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Dependent and Independent Time Series </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Perez-Ramirez,+F+O">Fredy O. Perez-Ramirez</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Caro-Lopera,+F+J">Francisco J. Caro-Lopera</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Diaz-Garcia,+J+A">Jose A. Diaz-Garcia</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Gonzalez-Farias,+G">Graciela Gonzalez-Farias</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Statistics Theory (math.ST)</span> </div> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2411.00534" title="Abstract" id="2411.00534"> arXiv:2411.00534 </a> [<a href="/pdf/2411.00534" title="Download PDF" id="pdf-2411.00534" aria-labelledby="pdf-2411.00534">pdf</a>, <a href="https://arxiv.org/html/2411.00534v1" title="View HTML" id="html-2411.00534" aria-labelledby="html-2411.00534" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00534" title="Other formats" id="oth-2411.00534" aria-labelledby="oth-2411.00534">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Change-point detection in functional time series: Applications to age-specific mortality and fertility </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Shang,+H+L">Han Lin Shang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 23 pages, 3 figures, 4 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Applications (stat.AP)</span>; Methodology (stat.ME) </div> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2411.00568" title="Abstract" id="2411.00568"> arXiv:2411.00568 </a> [<a href="/pdf/2411.00568" title="Download PDF" id="pdf-2411.00568" aria-labelledby="pdf-2411.00568">pdf</a>, <a href="https://arxiv.org/html/2411.00568v2" title="View HTML" id="html-2411.00568" aria-labelledby="html-2411.00568" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00568" title="Other formats" id="oth-2411.00568" aria-labelledby="oth-2411.00568">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Constrained Sampling with Primal-Dual Langevin Monte Carlo </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Chamon,+L+F+O">Luiz F. O. Chamon</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Karimi,+M+R">Mohammad Reza Karimi</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Korba,+A">Anna Korba</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 39 pages, 14 figures. Published at NeurIPS 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG); Optimization and Control (math.OC) </div> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2411.00573" title="Abstract" id="2411.00573"> arXiv:2411.00573 </a> [<a href="/pdf/2411.00573" title="Download PDF" id="pdf-2411.00573" aria-labelledby="pdf-2411.00573">pdf</a>, <a href="https://arxiv.org/html/2411.00573v2" title="View HTML" id="html-2411.00573" aria-labelledby="html-2411.00573" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00573" title="Other formats" id="oth-2411.00573" aria-labelledby="oth-2411.00573">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Characterizing extremal dependence on a hyperplane </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Wan,+P">Phyllis Wan</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Statistics Theory (math.ST)</span> </div> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2411.00611" title="Abstract" id="2411.00611"> arXiv:2411.00611 </a> [<a href="/pdf/2411.00611" title="Download PDF" id="pdf-2411.00611" aria-labelledby="pdf-2411.00611">pdf</a>, <a href="/format/2411.00611" title="Other formats" id="oth-2411.00611" aria-labelledby="oth-2411.00611">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Small coresets via negative dependence: DPPs, linear statistics, and concentration </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Bardenet,+R">R茅mi Bardenet</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Ghosh,+S">Subhroshekhar Ghosh</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Simon-Onfroy,+H">Hugo Simon-Onfroy</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Tran,+H">Hoang-Son Tran</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at NeurIPS 2024 (Spotlight Paper). Authors are listed in alphabetical order </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG); Probability (math.PR) </div> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2411.00621" title="Abstract" id="2411.00621"> arXiv:2411.00621 </a> [<a href="/pdf/2411.00621" title="Download PDF" id="pdf-2411.00621" aria-labelledby="pdf-2411.00621">pdf</a>, <a href="https://arxiv.org/html/2411.00621v1" title="View HTML" id="html-2411.00621" aria-labelledby="html-2411.00621" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00621" title="Other formats" id="oth-2411.00621" aria-labelledby="oth-2411.00621">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Nonparametric estimation of Hawkes processes with RKHSs </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Bonnet,+A">Anna Bonnet</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Sangnier,+M">Maxime Sangnier</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG); Methodology (stat.ME) </div> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/2411.00640" title="Abstract" id="2411.00640"> arXiv:2411.00640 </a> [<a href="/pdf/2411.00640" title="Download PDF" id="pdf-2411.00640" aria-labelledby="pdf-2411.00640">pdf</a>, <a href="https://arxiv.org/html/2411.00640v1" title="View HTML" id="html-2411.00640" aria-labelledby="html-2411.00640" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00640" title="Other formats" id="oth-2411.00640" aria-labelledby="oth-2411.00640">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Miller,+E">Evan Miller</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">Applications (stat.AP)</span>; Computation and Language (cs.CL) </div> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/2411.00644" title="Abstract" id="2411.00644"> arXiv:2411.00644 </a> [<a href="/pdf/2411.00644" title="Download PDF" id="pdf-2411.00644" aria-labelledby="pdf-2411.00644">pdf</a>, <a href="https://arxiv.org/html/2411.00644v1" title="View HTML" id="html-2411.00644" aria-labelledby="html-2411.00644" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00644" title="Other formats" id="oth-2411.00644" aria-labelledby="oth-2411.00644">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> What can we learn from marketing skills as a bipartite network from accredited programs? </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=del+Pilar+Garcia-Chitiva,+M">Maria del Pilar Garcia-Chitiva</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Dakduk,+S">Silvana Dakduk</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Correa,+J+C">Juan C. Correa</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages, 4 figures, 4 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph) </div> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/2411.00657" title="Abstract" id="2411.00657"> arXiv:2411.00657 </a> [<a href="/pdf/2411.00657" title="Download PDF" id="pdf-2411.00657" aria-labelledby="pdf-2411.00657">pdf</a>, <a href="https://arxiv.org/html/2411.00657v1" title="View HTML" id="html-2411.00657" aria-labelledby="html-2411.00657" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00657" title="Other formats" id="oth-2411.00657" aria-labelledby="oth-2411.00657">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fast Spectrum Estimation of Some Kernel Matrices </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Lepilov,+M">Mikhail Lepilov</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Numerical Analysis (math.NA) </div> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2411.00795" title="Abstract" id="2411.00795"> arXiv:2411.00795 </a> [<a href="/pdf/2411.00795" title="Download PDF" id="pdf-2411.00795" aria-labelledby="pdf-2411.00795">pdf</a>, <a href="https://arxiv.org/html/2411.00795v1" title="View HTML" id="html-2411.00795" aria-labelledby="html-2411.00795" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00795" title="Other formats" id="oth-2411.00795" aria-labelledby="oth-2411.00795">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Simulations for estimation of random effects and overall effect in three-level meta-analysis of standardized mean differences using constant and inverse-variance weights </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Kulinskaya,+E">Elena Kulinskaya</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Hoaglin,+D+C">David C. Hoaglin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 151 pages; 128 A4 size figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span> </div> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2411.00921" title="Abstract" id="2411.00921"> arXiv:2411.00921 </a> [<a href="/pdf/2411.00921" title="Download PDF" id="pdf-2411.00921" aria-labelledby="pdf-2411.00921">pdf</a>, <a href="https://arxiv.org/html/2411.00921v1" title="View HTML" id="html-2411.00921" aria-labelledby="html-2411.00921" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00921" title="Other formats" id="oth-2411.00921" aria-labelledby="oth-2411.00921">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Differentially Private Algorithms for Linear Queries via Stochastic Convex Optimization </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Micali,+G">Giorgio Micali</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Lezane,+C">Clement Lezane</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Betken,+A">Annika Betken</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span> </div> </div> </dd> <dt> <a name='item35'>[35]</a> <a href ="/abs/2411.00947" title="Abstract" id="2411.00947"> arXiv:2411.00947 </a> [<a href="/pdf/2411.00947" title="Download PDF" id="pdf-2411.00947" aria-labelledby="pdf-2411.00947">pdf</a>, <a href="https://arxiv.org/html/2411.00947v2" title="View HTML" id="html-2411.00947" aria-labelledby="html-2411.00947" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00947" title="Other formats" id="oth-2411.00947" aria-labelledby="oth-2411.00947">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Asymptotic theory of the quadratic assignment procedure for dyadic data analysis </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Shi,+L">Lei Shi</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Ding,+P">Peng Ding</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 74 pages, 2 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span> </div> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2411.00950" title="Abstract" id="2411.00950"> arXiv:2411.00950 </a> [<a href="/pdf/2411.00950" title="Download PDF" id="pdf-2411.00950" aria-labelledby="pdf-2411.00950">pdf</a>, <a href="https://arxiv.org/html/2411.00950v1" title="View HTML" id="html-2411.00950" aria-labelledby="html-2411.00950" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00950" title="Other formats" id="oth-2411.00950" aria-labelledby="oth-2411.00950">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Semiparametric Approach to Causal Inference </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Zhang,+A+G">Archer Gong Zhang</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Reid,+N">Nancy Reid</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Sun,+Q">Qiang Sun</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/2411.00969" title="Abstract" id="2411.00969"> arXiv:2411.00969 </a> [<a href="/pdf/2411.00969" title="Download PDF" id="pdf-2411.00969" aria-labelledby="pdf-2411.00969">pdf</a>, <a href="https://arxiv.org/html/2411.00969v1" title="View HTML" id="html-2411.00969" aria-labelledby="html-2411.00969" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.00969" title="Other formats" id="oth-2411.00969" aria-labelledby="oth-2411.00969">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Zhang,+M">Mingxuan Zhang</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Sun,+Y">Yan Sun</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Liang,+F">Faming Liang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item38'>[38]</a> <a href ="/abs/2411.01001" title="Abstract" id="2411.01001"> arXiv:2411.01001 </a> [<a href="/pdf/2411.01001" title="Download PDF" id="pdf-2411.01001" aria-labelledby="pdf-2411.01001">pdf</a>, <a href="https://arxiv.org/html/2411.01001v1" title="View HTML" id="html-2411.01001" aria-labelledby="html-2411.01001" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01001" title="Other formats" id="oth-2411.01001" aria-labelledby="oth-2411.01001">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Automated Assessment of Residual Plots with Computer Vision Models </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Li,+W">Weihao Li</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Cook,+D">Dianne Cook</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Tanaka,+E">Emi Tanaka</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=VanderPlas,+S">Susan VanderPlas</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Ackermann,+K">Klaus Ackermann</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/2411.01052" title="Abstract" id="2411.01052"> arXiv:2411.01052 </a> [<a href="/pdf/2411.01052" title="Download PDF" id="pdf-2411.01052" aria-labelledby="pdf-2411.01052">pdf</a>, <a href="https://arxiv.org/html/2411.01052v1" title="View HTML" id="html-2411.01052" aria-labelledby="html-2411.01052" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01052" title="Other formats" id="oth-2411.01052" aria-labelledby="oth-2411.01052">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Multivariate Gini-type discrepancies </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Auricchio,+G">Gennaro Auricchio</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Brigati,+G">Giovanni Brigati</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Giudici,+P">Paolo Giudici</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Toscani,+G">Giuseppe Toscani</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Analysis of PDEs (math.AP) </div> </div> </dd> <dt> <a name='item40'>[40]</a> <a href ="/abs/2411.01065" title="Abstract" id="2411.01065"> arXiv:2411.01065 </a> [<a href="/pdf/2411.01065" title="Download PDF" id="pdf-2411.01065" aria-labelledby="pdf-2411.01065">pdf</a>, <a href="https://arxiv.org/html/2411.01065v1" title="View HTML" id="html-2411.01065" aria-labelledby="html-2411.01065" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01065" title="Other formats" id="oth-2411.01065" aria-labelledby="oth-2411.01065">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Local Indicators of Mark Association for Spatial Marked Point Processes </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Eckardt,+M">Matthias Eckardt</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Moradi,+M">Mehdi Moradi</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Submitted for publication </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Applications (stat.AP) </div> </div> </dd> <dt> <a name='item41'>[41]</a> <a href ="/abs/2411.01066" title="Abstract" id="2411.01066"> arXiv:2411.01066 </a> [<a href="/pdf/2411.01066" title="Download PDF" id="pdf-2411.01066" aria-labelledby="pdf-2411.01066">pdf</a>, <a href="https://arxiv.org/html/2411.01066v1" title="View HTML" id="html-2411.01066" aria-labelledby="html-2411.01066" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01066" title="Other formats" id="oth-2411.01066" aria-labelledby="oth-2411.01066">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> An unified approach to link prediction in collaboration networks </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Sosa,+J">Juan Sosa</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Mart%C3%ADnez,+D">Diego Mart铆nez</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Guerrero,+N">Nicol谩s Guerrero</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 17 pages, 5 figures, 1 table </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Applications (stat.AP)</span>; Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item42'>[42]</a> <a href ="/abs/2411.01092" title="Abstract" id="2411.01092"> arXiv:2411.01092 </a> [<a href="/pdf/2411.01092" title="Download PDF" id="pdf-2411.01092" aria-labelledby="pdf-2411.01092">pdf</a>, <a href="https://arxiv.org/html/2411.01092v1" title="View HTML" id="html-2411.01092" aria-labelledby="html-2411.01092" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01092" title="Other formats" id="oth-2411.01092" aria-labelledby="oth-2411.01092">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Cost efficiency of fMRI studies using resting-state vs task-based functional connectivity </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Zhang,+X">Xinzhi Zhang</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Hulvershorn,+L+A">Leslie A Hulvershorn</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Constable,+T">Todd Constable</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Zhao,+Y">Yize Zhao</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Wang,+S">Selena Wang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Applications (stat.AP)</span>; Neurons and Cognition (q-bio.NC) </div> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2411.01100" title="Abstract" id="2411.01100"> arXiv:2411.01100 </a> [<a href="/pdf/2411.01100" title="Download PDF" id="pdf-2411.01100" aria-labelledby="pdf-2411.01100">pdf</a>, <a href="https://arxiv.org/html/2411.01100v2" title="View HTML" id="html-2411.01100" aria-labelledby="html-2411.01100" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01100" title="Other formats" id="oth-2411.01100" aria-labelledby="oth-2411.01100">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Transfer Learning Between U.S. Presidential Elections: How Should We Learn From A 2020 Ad Campaign To Inform 2024 Ad Campaigns? </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Miao,+X">Xinran Miao</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Zhao,+J">Jiwei Zhao</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Kang,+H">Hyunseung Kang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Applications (stat.AP)</span>; Methodology (stat.ME) </div> </div> </dd> <dt> <a name='item44'>[44]</a> <a href ="/abs/2411.01112" title="Abstract" id="2411.01112"> arXiv:2411.01112 </a> [<a href="/pdf/2411.01112" title="Download PDF" id="pdf-2411.01112" aria-labelledby="pdf-2411.01112">pdf</a>, <a href="https://arxiv.org/html/2411.01112v1" title="View HTML" id="html-2411.01112" aria-labelledby="html-2411.01112" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01112" title="Other formats" id="oth-2411.01112" aria-labelledby="oth-2411.01112">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal low-rank approximations of posteriors for linear Gaussian inverse problems on Hilbert spaces </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Carere,+G">Giuseppe Carere</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Lie,+H+C">Han Cheng Lie</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Statistics Theory (math.ST)</span> </div> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2411.01161" title="Abstract" id="2411.01161"> arXiv:2411.01161 </a> [<a href="/pdf/2411.01161" title="Download PDF" id="pdf-2411.01161" aria-labelledby="pdf-2411.01161">pdf</a>, <a href="https://arxiv.org/html/2411.01161v1" title="View HTML" id="html-2411.01161" aria-labelledby="html-2411.01161" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01161" title="Other formats" id="oth-2411.01161" aria-labelledby="oth-2411.01161">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Federated Learning with Relative Fairness </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Nakakita,+S">Shogo Nakakita</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Kaneko,+T">Tatsuya Kaneko</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Takamaeda-Yamazaki,+S">Shinya Takamaeda-Yamazaki</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Imaizumi,+M">Masaaki Imaizumi</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 43 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (stat.ML)</span>; Cryptography and Security (cs.CR); Machine Learning (cs.LG) </div> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2411.01234" title="Abstract" id="2411.01234"> arXiv:2411.01234 </a> [<a href="/pdf/2411.01234" title="Download PDF" id="pdf-2411.01234" aria-labelledby="pdf-2411.01234">pdf</a>, <a href="https://arxiv.org/html/2411.01234v1" title="View HTML" id="html-2411.01234" aria-labelledby="html-2411.01234" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01234" title="Other formats" id="oth-2411.01234" aria-labelledby="oth-2411.01234">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Identifying and bounding the probability of necessity for causes of effects with ordinal outcomes </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Zhang,+C">Chao Zhang</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Geng,+Z">Zhi Geng</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Li,+W">Wei Li</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Ding,+P">Peng Ding</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Statistics Theory (math.ST)</span> </div> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/2411.01237" title="Abstract" id="2411.01237"> arXiv:2411.01237 </a> [<a href="/pdf/2411.01237" title="Download PDF" id="pdf-2411.01237" aria-labelledby="pdf-2411.01237">pdf</a>, <a href="https://arxiv.org/html/2411.01237v1" title="View HTML" id="html-2411.01237" aria-labelledby="html-2411.01237" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01237" title="Other formats" id="oth-2411.01237" aria-labelledby="oth-2411.01237">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sparse Linear Regression: Sequential Convex Relaxation, Robust Restricted Null Space Property, and Variable Selection </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Bi,+S">Shujun Bi</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Yang,+Y">Yonghua Yang</a>, <a href="https://arxiv.org/search/math?searchtype=author&query=Pan,+S">Shaohua Pan</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 38 pages, 4 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Statistics Theory (math.ST)</span> </div> </div> </dd> <dt> <a name='item48'>[48]</a> <a href ="/abs/2411.01249" title="Abstract" id="2411.01249"> arXiv:2411.01249 </a> [<a href="/pdf/2411.01249" title="Download PDF" id="pdf-2411.01249" aria-labelledby="pdf-2411.01249">pdf</a>, <a href="https://arxiv.org/html/2411.01249v1" title="View HTML" id="html-2411.01249" aria-labelledby="html-2411.01249" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01249" title="Other formats" id="oth-2411.01249" aria-labelledby="oth-2411.01249">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A novel method for synthetic control with interference </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=He,+P">Peiyu He</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Li,+Y">Yilin Li</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Shi,+X">Xu Shi</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Miao,+W">Wang Miao</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 35 pages, 3 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span> </div> </div> </dd> <dt> <a name='item49'>[49]</a> <a href ="/abs/2411.01250" title="Abstract" id="2411.01250"> arXiv:2411.01250 </a> [<a href="/pdf/2411.01250" title="Download PDF" id="pdf-2411.01250" aria-labelledby="pdf-2411.01250">pdf</a>, <a href="https://arxiv.org/html/2411.01250v1" title="View HTML" id="html-2411.01250" aria-labelledby="html-2411.01250" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01250" title="Other formats" id="oth-2411.01250" aria-labelledby="oth-2411.01250">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Hierarchical and Density-based Causal Clustering </div> <div class='list-authors'><a href="https://arxiv.org/search/stat?searchtype=author&query=Kim,+K">Kwangho Kim</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Kim,+J">Jisu Kim</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Wasserman,+L+A">Larry A. Wasserman</a>, <a href="https://arxiv.org/search/stat?searchtype=author&query=Kennedy,+E+H">Edward H. Kennedy</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 38th Conference on Neural Information Processing Systems (NeurIPS 2024) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Methodology (stat.ME)</span>; Machine Learning (cs.LG); Machine Learning (stat.ML) </div> </div> </dd> <dt> <a name='item50'>[50]</a> <a href ="/abs/2411.01275" title="Abstract" id="2411.01275"> arXiv:2411.01275 </a> [<a href="/pdf/2411.01275" title="Download PDF" id="pdf-2411.01275" aria-labelledby="pdf-2411.01275">pdf</a>, <a href="https://arxiv.org/html/2411.01275v1" title="View HTML" id="html-2411.01275" aria-labelledby="html-2411.01275" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2411.01275" title="Other formats" id="oth-2411.01275" aria-labelledby="oth-2411.01275">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime </div> <div class='list-authors'><a href="https://arxiv.org/search/math?searchtype=author&query=Vuursteen,+L">Lasse Vuursteen</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To appear in the Thirty-eight Conference on Neural Information Processing Systems -- 10 page article + 20 pages appendix and references </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Statistics Theory (math.ST)</span> </div> </div> </dd> </dl> <div class='paging'>Total of 1004 entries : <span>1-50</span> <a href=/list/stat/2024-11?skip=50&show=50>51-100</a> <a href=/list/stat/2024-11?skip=100&show=50>101-150</a> <a href=/list/stat/2024-11?skip=150&show=50>151-200</a> <span>...</span> <a href=/list/stat/2024-11?skip=1000&show=50>1001-1004</a> </div> <div class='morefewer'>Showing up to 50 entries per page: <a href=/list/stat/2024-11?skip=0&show=25 rel="nofollow"> fewer</a> | <a href=/list/stat/2024-11?skip=0&show=100 rel="nofollow"> more</a> | <a href=/list/stat/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"> <!-- Macro-Column 1 --> <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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