Publications · Journal article · Math. Program. 2026
Mean-semideviation-based distributionally robust learning with weakly convex losses: convergence rates and finite-sample guarantees
Mathematical Programming, 215(1), pp. 237–267, 2026.
In brief
Establishes convergence rates and finite-sample guarantees for distributionally robust learning formulated with mean–semideviation risk, for the broad class of weakly convex losses.
Topics
Cite
@article{zhu2026semideviation,
title = {Mean-semideviation-based distributionally robust learning with weakly convex losses: convergence rates and finite-sample guarantees},
author = {Landi Zhu and Mert Gürbüzbalaban and Andrzej Ruszczyński},
year = {2026},
journal = {Mathematical Programming},
volume = {215(1)},
pages = {237--267},
doi = {10.1007/s10107-025-02218-z},
}Relatedsame topics