5 citations · 6 across the 14 of their papers we have counts for
8 papers · 1 filter
Understanding Private Evolution as Learning-Augmented Clustering
Audra McMillan, Kunal Talwar, Felix Zhou
Private Evolution (PE) is a differentially private algorithm for synthetic data generation. While it can be viewed as a Wasserstein learning algorithm, it performs much better in p…
Faster Rates for Private Adversarial Bandits
Hilal Asi, Vinod Raman, Kunal Talwar
We design new differentially private algorithms for the problems of adversarial bandits and bandits with expert advice. For adversarial bandits, we give a simple and efficient conv…
On Privately Estimating a Single Parameter
Hilal Asi, John C. Duchi, Kunal Talwar
We investigate differentially private estimators for individual parameters within larger parametric models. While generic private estimators exist, the estimators we provide repose…
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
Daogao Liu, Kunal Talwar
There is a gap between finding a first-order stationary point (FOSP) and a second-order stationary point (SOSP) under differential privacy constraints, and it remains unclear wheth…
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
Guy Kornowski, Daogao Liu, Kunal Talwar
We study differentially private (DP) optimization algorithms for stochastic and empirical objectives which are neither smooth nor convex, and propose methods that return a Goldstei…
Instance-Optimal Private Density Estimation in the Wasserstein Distance
Vitaly Feldman, Audra McMillan, Satchit Sivakumar +1
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric fo…
Private Online Learning via Lazy Algorithms
Hilal Asi, Tomer Koren, Daogao Liu +1
We study the problem of private online learning, specifically, online prediction from experts (OPE) and online convex optimization (OCO). We propose a new transformation that trans…
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman +4
While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to…