1 citations · 1 across the 4 of their papers we have counts for
4 papers
Public-data Assisted Private Stochastic Optimization: Power and Limitations
Enayat Ullah, Michael Menart, Raef Bassily +2
We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO)…
From Adaptive Query Release to Machine Unlearning
Enayat Ullah, Raman Arora
We formalize the problem of machine unlearning as design of efficient unlearning algorithms corresponding to learning algorithms which perform a selection of adaptive queries from…
Private Federated Learning with Autotuned Compression
Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz +1
We propose new techniques for reducing communication in private federated learning without the need for setting or tuning compression rates. Our on-the-fly methods automatically ad…
Adversarial Robustness is at Odds with Lazy Training
Yunjuan Wang, Enayat Ullah, Poorya Mianjy +1
Recent works show that adversarial examples exist for random neural networks [Daniely and Schacham, 2020] and that these examples can be found using a single step of gradient ascen…