{"id":18076,"date":"2026-07-30T09:31:32","date_gmt":"2026-07-30T07:31:32","guid":{"rendered":"https:\/\/ensai.fr\/?p=18076"},"modified":"2026-07-30T09:31:32","modified_gmt":"2026-07-30T07:31:32","slug":"thibault-pautrel-icml","status":"publish","type":"post","link":"https:\/\/ensai.fr\/en\/thibault-pautrel-icml\/","title":{"rendered":"Thibault Pautrel Presents a Paper at ICML 2026 in South Korea"},"content":{"rendered":"<p><strong>Thibault Pautrel, a graduate of ENSAI\u2019s Master\u2019s program in Smart Data Science, presented the paper \u201cRiemannian stochastic optimization for sufficient dimension reduction,\u201d co-authored with Fran\u00e7ois Portier, at the 43rd International Conference on Machine Learning (ICML), held in Seoul from July 6 to 11, 2026. \u00a0<\/strong><\/p>\n<p><a href=\"https:\/\/icml.cc\/\" target=\"_blank\" rel=\"noopener\">ICML, the International Conference on Machine Learning<\/a>, is the leading international academic conference on machine learning and artificial intelligence.<\/p>\n<p>Thibault Pautrel, who earned his <a href=\"https:\/\/ensai.fr\/en\/2-cursus\/msc-statistics-for-smart-data\/\">Master for Smart Data Science<\/a> in 2025, holds a Ph.D. in mathematics. He defended his doctoral dissertation (at IRMAR) on the zeros of polynomials with random coefficients in 2022.<\/p>\n<p>Following his Master\u2019s program, Thibault Pautrel joined the <a href=\"https:\/\/l2s.centralesupelec.fr\/\" target=\"_blank\" rel=\"noopener\">L2S Signals and Systems Laboratory<\/a> (CentraleSup\u00e9lec, Universit\u00e9 Paris-Saclay) as a postdoctoral researcher, focusing on federated learning with parameters taking values in manifolds.<\/p>\n<p>At the same time, Thibault worked with <a href=\"https:\/\/ensai.fr\/en\/equipe\/portier-francois\/\">Fran\u00e7ois Portier<\/a>, a professor of statistics at CREST ENSAI and director of ENSAI&#8217;s Master for Smart Data Science, on a project involving dimensionality reduction in regression.<\/p>\n<blockquote><p>When there are many variables, regression models are difficult to estimate, and the accuracy of predictions is reduced compared to the standard case. In this context, Thibault Pautrel and Fran\u00e7ois Portier proposed an improvement to the MAVE method, published in 2002. The proposed approach is based on nearest neighbors as well as a gradient descent method on the Stiefel manifold, thus combining the expertise of both researchers.<\/p><\/blockquote>\n<p>The project was accepted at ICML 2026, leading Thibault Pautrel to present the paper \u201c<a href=\"https:\/\/icml.cc\/virtual\/2026\/poster\/63071\" target=\"_blank\" rel=\"noopener\">Riemannian stochastic optimization for sufficient dimension reduction<\/a>\u201d at the conference.<\/p>\n<h3>Riemannian stochastic optimization for sufficient dimension reduction &#8211; Abstract<\/h3>\n<p>Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that preserves the conditional mean of the response. Existing gradient-based estimators either operate in the ambient space and suffer from the curse of dimensionality, or localize in the reduced space at a per-outer-iteration cost at least quadratic in the sample size. We show that minimizers of the population Minimum Average Variance Estimation (MAVE) risk approximate the same Grassmannian target as the Outer Product of Gradients (OPG), and recast the empirical criterion as a smooth maximization on the Stiefel manifold with closed-form Riemannian gradient. The resulting algorithm, SMAVE, combines sparse projected-space nearest-neighbor localization with Riemannian stochastic gradient ascent. A simplified version comes with almost-sure convergence and a non-asymptotic rate matching the standard non-convex stochastic first-order scaling. Empirically, SMAVE matches or improves on RMAVE&#8217;s synthetic subspace recovery at moderate-to-high ambient dimension, and on four real datasets it uniformly improves over OPG and is competitive with or outperforms RMAVE at orders of magnitude lower runtime.<\/p>\n<p><span class=\"hentry_link_contour\"><a href=\"https:\/\/icml.cc\/virtual\/2026\/poster\/63071\" target=\"_blank\" rel=\"noopener noreferrer\"> View the paper <\/a><\/span><\/p>\n<p><strong>Learn more about <a href=\"https:\/\/thibault-pautrel.github.io\/\" target=\"_blank\" rel=\"noopener\">Thibault Pautrel<\/a>, <a href=\"https:\/\/ensai.fr\/en\/equipe\/portier-francois\/\" target=\"_blank\" rel=\"noopener\">Fran\u00e7ois Portier<\/a>, and <a href=\"https:\/\/ensai.fr\/en\/recherche\/themes-de-recherche\/\" target=\"_blank\" rel=\"noopener\">research at ENSAI<\/a>.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Thibault Pautrel, a graduate of ENSAI\u2019s Master\u2019s program in Smart Data Science,&#8230; <br \/><a class=\"readmore\" href=\"https:\/\/ensai.fr\/en\/thibault-pautrel-icml\/\">Lire la suite<\/a><\/p>\n","protected":false},"author":73,"featured_media":18077,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[185],"tags":[379,342,1300],"class_list":["post-18076","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-innovation","tag-alumni-en","tag-publication","tag-research"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Thibault Pautrel Presents a Paper at ICML 2026 in South Korea | ENSAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ensai.fr\/en\/thibault-pautrel-icml\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Thibault Pautrel Presents a Paper at ICML 2026 in South Korea | ENSAI\" \/>\n<meta property=\"og:description\" content=\"Thibault Pautrel, a graduate of ENSAI\u2019s Master\u2019s program in Smart Data Science,... 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