3 citations · 9 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Omri Lev, Vishwak Srinivasan, Moshe Shenfeld +3
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning,…
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan +1
Reinforcement learning (RL) is empirically successful in complex nonlinear Markov decision processes (MDPs) with continuous state spaces. By contrast, the majority of theoretical R…
On the Analysis of Trajectories of Gradient Descent in the Optimization of Deep Neural Networks
Adepu Ravi Sankar, Vishwak Srinivasan, Vineeth N Balasubramanian
Theoretical analysis of the error landscape of deep neural networks has garnered significant interest in recent years. In this work, we theoretically study the importance of noise…