5 citations · 6 across the 6 of their papers we have counts for
5 papers
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
Hilal Asi, Vitaly Feldman, Jelani Nelson +3
We study the problem of private vector mean estimation in the shuffle model of privacy where users each have a unit vector . We propose a new multi-mes…
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy
Aadirupa Saha, Hilal Asi
We consider the well-studied dueling bandit problem, where a learner aims to identify near-optimal actions using pairwise comparisons, under the constraint of differential privacy.…
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates
Hilal Asi, Daogao Liu
We study differentially private stochastic convex optimization (DP-SCO) under user-level privacy, where each user may hold multiple data items. Existing work for user-level DP-SCO…
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
Hilal Asi, Vitaly Feldman, Tomer Koren +1
We consider online learning problems in the realizable setting, where there is a zero-loss solution, and propose new Differentially Private (DP) algorithms that obtain near-optimal…
From Robustness to Privacy and Back
Hilal Asi, Jonathan Ullman, Lydia Zakynthinou
We study the relationship between two desiderata of algorithms in statistical inference and machine learning: differential privacy and robustness to adversarial data corruptions. T…