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Jindong Tong

2 accepted papers

2025

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

ICML 2025poster

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 eit…

Cited by 0SourcePDFScholar
2024

New Sample Complexity Bounds for Sample Average Approximation in Heavy-Tailed Stochastic Programming

ICML 2024poster

This paper studies sample average approximation (SAA) and its simple regularized variation in solving convex or strongly convex stochastic programming problems. Under heavy-tailed assumptions and comparable regularity conditions as in the typical SAA literature, we show --- perhaps for the first tim…

Cited by 1SourcePDFScholar