{"id":2910,"date":"2025-04-07T07:02:32","date_gmt":"2025-04-07T07:02:32","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/04\/07\/2504-03158\/"},"modified":"2025-04-07T07:02:32","modified_gmt":"2025-04-07T07:02:32","slug":"2504-03158","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/04\/07\/2504-03158\/","title":{"rendered":"Accelerating Particle-based Energetic Variational Inference"},"content":{"rendered":"<p>    Accelerating Particle-based Energetic Variational Inference<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2504.03158v1 Announce Type: new<br \/>\nAbstract: In this work, we propose a novel particle-based variational inference (ParVI) method that accelerates the EVI-Im. Inspired by energy quadratization (EQ) and operator splitting techniques for gradient flows, our approach efficiently drives particles towards the target distribution. Unlike EVI-Im, which employs the implicit Euler method to solve variational-preserving particle dynamics for minimizing the KL divergence, derived using a &#8220;discretize-then-variational&#8221; approach, the proposed algorithm avoids repeated evaluation of inter-particle interaction terms, significantly reducing computational cost. The framework is also extensible to other gradient-based sampling techniques. Through several numerical experiments, we demonstrate that our method outperforms existing ParVI approaches in efficiency, robustness, and accuracy.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Xuelian Bao, Lulu Kang, Chun Liu, Yiwei Wang<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2504.03158\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Accelerating Particle-based Energetic Variational Inference arXiv:2504.03158v1 Announce Type: new Abstract: In this work, we propose a novel particle-based variational inference (ParVI) method that accelerates the EVI-Im. Inspired by energy quadratization (EQ) and operator splitting techniques for gradient flows, our approach efficiently drives particles towards the target distribution. Unlike EVI-Im, which employs the implicit Euler method [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[189,380,936],"class_list":["post-2910","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-based","tag-particle","tag-variational"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/2910"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=2910"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/2910\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=2910"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=2910"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=2910"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}