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Regularization, sparse recovery, and median-of-means tournaments. (English) Zbl 07066250
Summary: 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.
Reviewer: Reviewer (Berlin)

60 Probability theory and stochastic processes
62 Statistics
Full Text: DOI Euclid arXiv
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