2026-04-14T02:32:46Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/721042025-12-06T17:21:49Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Lugosi, Gábor author Mendelson, Shahar author 2025-12-02T14:22:31Z 2025-12-02T14:22:31Z 2019 2025-12-02T14:22:31Z We introduce a regularized risk minimization procedure for regression function estimation. The procedure is based on median-of-means tournaments, introduced by the authors in Lugosi and Mendelson (2018) and achieves near optimal accuracy and confidence under general conditions, including heavy-tailed predictor and response variables. It outperforms standard regularized empirical risk minimization procedures such as LASSO or SLOPE in heavy-tailed problems. Gábor Lugosi was supported by the Spanish Ministry of Economy and Competitiveness, Grant MTM2015-67304-P and FEDER, EU; "High-dimensional problems in structured probabilistic models" -- Ayudas Fundacion BBVA a Equipos de Investigación Científica 2017; and Google Focused Award "Algorithms and Learning for AI". Shahar Mendelson was supported in part by the Israel Science Foundation. http://hdl.handle.net/10230/72104 Lasso Median-of-means tournament Regularized risk minimization Robust regression Slope Regularization, sparse recovery, and median-of-means tournaments