290 citations · 492 across the 36 of their papers we have counts for
4 papers · 1 filter
Shifted Interpolation for Differential Privacy
Jinho Bok, Weijie Su, Jason M. Altschuler
Noisy gradient descent and its variants are the predominant algorithms for differentially private machine learning. It is a fundamental question to quantify their privacy leakage,…
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
Jason M. Altschuler, Kunal Talwar
A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. St…
Kernel approximation on algebraic varieties
Jason M. Altschuler, Pablo A. Parrilo
Low-rank approximation of kernels is a fundamental mathematical problem with widespread algorithmic applications. Often the kernel is restricted to an algebraic variety, e.g., in p…
Online learning over a finite action set with limited switching
Jason Altschuler, Kunal Talwar
This paper studies the value of switching actions in the Prediction From Experts (PFE) problem and Adversarial Multi-Armed Bandits (MAB) problem. First, we revisit the well-studied…