Publications · Journal article · J. Optim. Theory Appl. 2022
A stochastic subgradient method for distributionally robust non-convex and non-smooth learning
Journal of Optimization Theory and Applications, 194(3), pp. 1014–1041, 2022.
In brief
Formulates statistical learning robust to perturbations of the data distribution using mean–semideviation risk, and develops a stochastic subgradient method for generalized-differentiable losses that may be non-convex and non-smooth — the first with rigorous convergence guarantees in this setting — achieving any desired robustness level at little extra cost over population risk minimization.
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Cite
@article{gurbuzbalaban2022subgradient,
title = {A stochastic subgradient method for distributionally robust non-convex and non-smooth learning},
author = {Mert Gürbüzbalaban and Andrzej Ruszczyński and Landi Zhu},
year = {2022},
journal = {Journal of Optimization Theory and Applications},
volume = {194(3)},
pages = {1014--1041},
doi = {10.1007/s10957-022-02063-6},
}Relatedsame topics