Non-Asymptotic and Non-Lipschitzian Bounds on Optimal Values in Stochastic Optimization Under Heavy Tails

Abstract

This paper focuses on non-asymptotic confidence bounds (CB) for the optimal values of stochastic optimization (SO) problems. Existing approaches often rely on two conditions that may be restrictive: The need for a global Lipschitz constant and the assumption of light-tailed distributions. Beyond either of the conditions, it remains largely unknown whether computable CBs can be constructed. In view of this literature gap, we provide three key findings below: (i) Based on the conventional formulation of sample average approximation (SAA), we derive non-Lipschitzian CBs for convex SP problems under heavy tails. (ii) We explore diametrical risk minimization (DRM)—a recently introduced modification to SAA—and attain non-Lipschitzian CBs for nonconvex SP problems in light-tailed settings. (iii) We extend our analysis of DRM to handle heavy-tailed randomness by utilizing properties in formulations for training over-parameterized classification models.