12 citations · 44 across the 25 of their papers we have counts for
6 papers · 1 filter
Faster Algorithms for User-Level Private Stochastic Convex Optimization
Andrew Lowy, Daogao Liu, Hilal Asi
We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this setting, there are users (e.g., cell phones), each possess…
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
Hilal Asi, Daogao Liu, Kevin Tian
We study the problem of differentially private stochastic convex optimization (DP-SCO) with heavy-tailed gradients, where we assume a -moment bound on the Lipschitz…
Wally: Batched Private Nearest Neighbor Search at Scale
Hilal Asi, Fabian Boemer, Nicholas Genise +8
We present Wally, a batched private nearest-neighbor search protocol that uses differential privacy to break the linear computation barrier of fully-oblivious schemes. In Tiptoe, t…
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…
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.…