adversarial-contamination

This paper revisits median-of-means estimation from a deterministic optimization perspective and develops a family of block-Lp estimators for robust learning under heavy-tailed and adversarial corruption, showing that nonconvex methods can approach the trimmed oracle performance while remaining computationally tractable.