Author

Malo Sommers

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Preprint Sep 2026

Random tilts to find stationary points in stochastic convex optimization

A few complementary results are presented that show that some dimension dependence is necessary, in distinction from standard stochastic optimization and empirical risk minimization, by providing minimax lower bounds scaling as $\sqrt{d/n}$ for d-dimensional problems given $n$ observations.

Felipe Areces, John C. Duchi, Malo Sommers · 0 citations

Jul 2026

Finding a stationary point of a stochastic convex problem

We consider the problem of finding stationary points for stochastic convex optimization problems. Rather than surrogates to stationarity, such as a proximity-to-stationarity guarantee or small gradient of the Moreau envelope, we ask for a stronger notion: that the subdifferential of the objective actually contains a sm...

Felipe Areces, John C. Duchi, Malo Sommers · arXiv.org · 2 citations