ON THE ROBUSTNESS OF MINIMUM NORM INTERPOLATORS AND REGULARIZED EMPIRICAL RISK MINIMIZERS

• 成果类型: Article
• 署名作者: Chinot, Geoffrey; Loeffler, Matthias; van de Geer, Sara
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/22-AOS2190
• 发表日期: 2022
• 页码: 2306-2333
• 关键词: ridge-regression; STABILITY; inference; matrices; geometry; Lasso

摘要

This article develops a general theory for minimum norm interpolating estimators and regularized empirical risk minimizers (RERM) in linear models in the presence of additive, potentially adversarial, errors. In particular, no conditions on the errors are imposed. A quantitative bound for the prediction error is given, relating it to the Rademacher complexity of the covariates, the norm of the minimum norm interpolator of the errors and the size of the subdifferential around the true parameter. The general theory is illustrated for Gaussian features and several norms: The l(1), l(2), group Lasso and nuclear norms. In case of sparsity or low-rank inducing norms, minimum norm interpolators and RERM yield a prediction error of the order of the average noise level, provided that the overparameterization is at least a logarithmic factor larger than the number of samples and that, in case of RERM, the regularization parameter is small enough. Lower bounds that show near optimality of the results complement the analysis.