3 papers
cs.LG2024
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…
math.OC2024
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…
cs.LG2024
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)…