1 citations · 1 across the 15 of their papers we have counts for
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Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
Raef Bassily, Cristóbal Guzmán, Michael Menart
In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of -differential pri…
Non-Euclidean High-Order Smooth Convex Optimization
Juan Pablo Contreras, Cristóbal Guzmán, David Martínez-Rubio
We develop algorithms for the optimization of convex objectives that have Hölder continuous -th derivatives by using a -th order oracle, for any . Our algorithms wo…
Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity
Quan Nguyen, Nishant A. Mehta, Cristóbal Guzmán
The minimax sample complexity of group distributionally robust optimization (GDRO) has been determined up to a factor, where is the number of groups. In this work, we…
Tracking solutions of time-varying variational inequalities
Hédi Hadiji, Sarah Sachs, Cristóbal Guzmán
Tracking the solution of time-varying variational inequalities is an important problem with applications in game theory, optimization, and machine learning. Existing work considers…
Differentially Private Optimization with Sparse Gradients
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +2
Motivated by applications of large embedding models, we study differentially private (DP) optimization problems under sparsity of individual gradients. We start with new near-optim…
Public-data Assisted Private Stochastic Optimization: Power and Limitations
Enayat Ullah, Michael Menart, Raef Bassily +2
We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO)…
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…
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Tomás González, Cristóbal Guzmán, Courtney Paquette
We study the problem of differentially-private (DP) stochastic (convex-concave) saddle-points in the setting. We propose -DP algorithms based on stochast…