Omri Lev — accepted AI-conference papers

2026

Near-Optimal Private Linear Regression via Iterative Hessian Mixing

Omri Lev, Moshe Shenfeld, Vishwak Srinivasan, Katrina Ligett, Ashia Wilson

ICML 2026spotlight

We study differentially private ordinary least squares (DP-OLS) with bounded data $(X,Y)$ via sketching-based mechanisms. While Gaussian sketching approaches have been explored for DP-OLS \citep{sheffet2017differentially}, they are typically viewed as less competitive than the Adaptive Sufficient St…

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2025

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches

Omri Lev, Vishwak Srinivasan, Moshe Shenfeld, Katrina Ligett, Ayush Sekhari, Ashia C. Wilson

NeurIPS 2025poster

Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique in data science and machine learning. Beyond computational benefits, this operation also provides differential privacy guarantees due to its inherent randomness. In this work, we…

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