This paper is a survey of methods for solving smooth,(strongly) monotone stochastic
variational inequalities. To begin with, we present the deterministic foundation from which …

We develop and analyze algorithms for distributionally robust optimization (DRO) of convex
losses. In particular, we consider group-structured and bounded $ f $-divergence uncertainty …

Exploiting partial first-order information in a cyclic way is arguably the most natural strategy
to obtain scalable first-order methods. However, despite their wide use in practice, cyclic …

We prove complexity bounds for the primal-dual algorithm with random extrapolation and
coordinate descent (PURE-CD), which has been shown to obtain good practical …

We design algorithms for minimizing max i∈[n] fi (x) over ad-dimensional Euclidean or
simplex domain. When each fi is 1-Lipschitz and 1-smooth, our method computes an ɛ …

While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk
over a set of distributions, Agarwal and Zhang (2022) recently proposed a variant that …

We consider the penalized distributionally robust optimization (DRO) problem with a closed,
convex uncertainty set, a setting that encompasses learning using f-DRO and spectral/L-risk …

We consider the penalized distributionally robust optimization (DRO) problem with a closed,
convex uncertainty set, a setting that encompasses the $ f $-DRO, Wasserstein-DRO, and …

We study the problem of learning a single neuron with respect to the $ L_2^ 2$-loss in the
presence of adversarial distribution shifts, where the labels can be arbitrary, and the goal is …

This paper addresses the Optimal Transport problem, which is regularized by the square of
Euclidean ℓ 2-norm. It offers theoretical guarantees regarding the iteration complexities of …