Vishwak Srinivasan

According to our database, Vishwak Srinivasan authored at least 11 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

The Fast Mixing Mechanism for Differential Privacy.

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

CoRR, May, 2026

Near-Optimal Private Linear Regression via Iterative Hessian Mixing.

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

CoRR, January, 2026

2025

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches.

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

CoRR, May, 2025

High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm.

Vishwak Srinivasan, Andre Wibisono, Ashia Wilson

Proceedings of the International Conference on Algorithmic Learning Theory, 2025

2024

Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm.

Vishwak Srinivasan, Andre Wibisono, Ashia C. Wilson

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2021

Subseasonal climate prediction in the western US using Bayesian spatial models.

Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity.

Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

On Learning Ising Models under Huber's Contamination Model.

Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018

On the Analysis of Trajectories of Gradient Descent in the Optimization of Deep Neural Networks.

Adepu Ravi Sankar, Vishwak Srinivasan, Vineeth N. Balasubramanian

CoRR, 2018

<i>ADINE</i>: an adaptive momentum method for stochastic gradient descent.

Vishwak Srinivasan, Adepu Ravi Sankar, Vineeth N. Balasubramanian

Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2018

2017

ADINE: An Adaptive Momentum Method for Stochastic Gradient Descent.

Vishwak Srinivasan, Adepu Ravi Sankar, Vineeth N. Balasubramanian

CoRR, 2017