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cs.LG2026
The Fast Mixing Mechanism for Differential Privacy
Omri Lev, Moshe Shenfeld, Vishwak Srinivasan +2
Randomized sketching is a central tool for compressing large-scale optimization problems while preserving accuracy. In particular, sketches that are based on structured matrices, s…
cs.LG2026
Efficient privacy loss accounting for subsampling and random allocation
Vitaly Feldman, Moshe Shenfeld
We consider the privacy amplification properties of a sampling scheme in which a user's data isused in steps chosen randomly and uniformly from a sequence (or set) of steps…
cs.LG2026
Near-Optimal Private Linear Regression via Iterative Hessian Mixing
Omri Lev, Moshe Shenfeld, Vishwak Srinivasan +2
We study differentially private ordinary least squares (DP-OLS) with bounded data via sketching-based mechanisms. While Gaussian sketching approaches have been explored for…