Hilal Asi

Adapting to Function Difficulty and Growth Conditions in Private Optimization

2021 · arXiv (Cornell University)

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 bounds for arbitrary convex functions, it is often the …

User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates

2023 · arXiv (Cornell University)

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 either requires super-polynomial runtime [Ghazi et al. (2023)] or requires the …