12 citations · 37 across the 10 of their papers we have counts for
15 papers
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…
AdaBoN: Adaptive Best-of-N Alignment
Vinod Raman, Hilal Asi, Satyen Kale
Recent advances in test-time alignment methods, such as Best-of-N sampling, offer a simple and effective way to steer language models (LMs) toward preferred behaviors using reward…
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…
PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
Hilal Asi, Vitaly Feldman, Hannah Keller +2
We revisit the problem of secure aggregation of high-dimensional vectors in a two-server system such as Prio. These systems are typically used to aggregate vectors such as gradient…
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…