74 citations · 172 across the 28 of their papers we have counts for
17 papers · 1 filter
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…
Tracking the Best Expert Privately
Aadirupa Saha, Vinod Raman, Hilal Asi
We design differentially private algorithms for the problem of prediction with expert advice under dynamic regret, also known as tracking the best expert. Our work addresses three…
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 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…
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…
Fast Optimal Locally Private Mean Estimation via Random Projections
Hilal Asi, Vitaly Feldman, Jelani Nelson +2
We study the problem of locally private mean estimation of high-dimensional vectors in the Euclidean ball. Existing algorithms for this problem either incur sub-optimal error or ha…
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…
Private optimization in the interpolation regime: faster rates and hardness results
Hilal Asi, Karan Chadha, Gary Cheng +1
In non-private stochastic convex optimization, stochastic gradient methods converge much faster on interpolation problems -- problems where there exists a solution that simultaneou…
Private Online Prediction from Experts: Separations and Faster Rates
Hilal Asi, Vitaly Feldman, Tomer Koren +1
Online prediction from experts is a fundamental problem in machine learning and several works have studied this problem under privacy constraints. We propose and analyze new algori…
Optimal Algorithms for Mean Estimation under Local Differential Privacy
Hilal Asi, Vitaly Feldman, Kunal Talwar
We study the problem of mean estimation of -bounded vectors under the constraint of local differential privacy. While the literature has a variety of algorithms that achiev…
Adapting to Function Difficulty and Growth Conditions in Private Optimization
Hilal Asi, Daniel Levy, John Duchi
We develop algorithms for private stochastic convex optimization that adapt to the hardness of the specific function we wish to optimize. While previous work provide worst-case bou…
Private Adaptive Gradient Methods for Convex Optimization
Hilal Asi, John Duchi, Alireza Fallah +2
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm w…
Private Stochastic Convex Optimization: Optimal Rates in Geometry
Hilal Asi, Vitaly Feldman, Tomer Koren +1
Stochastic convex optimization over an -bounded domain is ubiquitous in machine learning applications such as LASSO but remains poorly understood when learning with differe…
Element Level Differential Privacy: The Right Granularity of Privacy
Hilal Asi, John Duchi, Omid Javidbakht
Differential Privacy (DP) provides strong guarantees on the risk of compromising a user's data in statistical learning applications, though these strong protections make learning c…