AI RESEARCH
Adaptive and Stratified Subsampling for High-Dimensional Robust Estimation
arXiv CS.LG
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ArXi:2410.12367v2 Announce Type: replace-cross We study robust high-dimensional sparse regression under finite-variance heavy-tailed noise, epsilon-contamination, and alpha-mixing dependence via two subsampling estimators: Adaptive Importance Sampling (AIS) and Stratified Sub-sampling (SS). Under sub-Gaussian design whose scopeis precisely delimited and finite-variance noise, a subsample of size m achieves the minimax-optimal rate.