18 citations · 42 across the 16 of their papers we have counts for
7 papers · 1 filter
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
Raef Bassily, Cristóbal Guzmán, Michael Menart
We study differentially private stochastic optimization in convex and non-convex settings. For the convex case, we focus on the family of non-smooth generalized linear losses (GLLs…
Best-Case Lower Bounds in Online Learning
Cristóbal Guzmán, Nishant A. Mehta, Ali Mortazavi
Much of the work in online learning focuses on the study of sublinear upper bounds on the regret. In this work, we initiate the study of best-case lower bounds in online convex opt…
An Optimal Algorithm for Strict Circular Seriation
Santiago Armstrong, Cristóbal Guzmán, Carlos A. Sing-Long
We study the problem of circular seriation, where we are given a matrix of pairwise dissimilarities between objects, and the goal is to find a {\em circular order} of the objec…
Optimal Algorithms for Differentially Private Stochastic Monotone Variational Inequalities and Saddle-Point Problems
Digvijay Boob, Cristóbal Guzmán
In this work, we conduct the first systematic study of stochastic variational inequality (SVI) and stochastic saddle point (SSP) problems under the constraint of differential priva…
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization
Siqi Zhang, Junchi Yang, Cristóbal Guzmán +2
This paper studies the complexity for finding approximate stationary points of nonconvex-strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth finit…
Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Time
Raef Bassily, Cristóbal Guzmán, Anupama Nandi
Differentially private (DP) stochastic convex optimization (SCO) is a fundamental problem, where the goal is to approximately minimize the population risk with respect to a convex…
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…