6 citations · 6 across the 6 of their papers we have counts for
6 papers
Oracle Complexity of Stochastic Fixed-Point Equations with Nonexpansive Maps
Jelena Diakonikolas, Cristóbal Guzmán, David Martínez-Rubio
We study the oracle complexity of computing a point with small fixed-point residual , for a general norm and a self-map of a compact convex set.…
Efficient Methods for Min-Max Optimization with Dual-Linear Coupling
Ronak Mehta, Jelena Diakonikolas, Zaid Harchaoui
We study a class of convex-concave min-max problems in which the coupled component of the objective is linear in at least one of the two decision vectors. We identify such problem…
Optimization on a Finer Scale: Bounded Local Subgradient Variation Perspective
Jelena Diakonikolas, Cristóbal Guzmán
We initiate the study of nonsmooth optimization problems under bounded local subgradient variation, which postulates bounded difference between (sub)gradients in small local region…
Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
Ronak Mehta, Jelena Diakonikolas, Zaid Harchaoui
We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using -DRO and spectral/$…
Complementary Composite Minimization, Small Gradients in General Norms, and Applications
Jelena Diakonikolas, Cristóbal Guzmán
Composite minimization is a powerful framework in large-scale convex optimization, based on decoupling of the objective function into terms with structurally different properties a…
Lower Bounds for Parallel and Randomized Convex Optimization
Jelena Diakonikolas, Cristóbal Guzmán
We study the question of whether parallelization in the exploration of the feasible set can be used to speed up convex optimization, in the local oracle model of computation. We sh…