ADINE: An Adaptive Momentum Method for Stochastic Gradient Descent. - Vishwak Srinivasan; Adepu Ravi Sankar; Vineeth N. Balasubramanian (CoRR, 2017)

ADINE: an adaptive momentum method for stochastic gradient descent. - Vishwak Srinivasan; Adepu Ravi Sankar; Vineeth N. Balasubramanian (COMAD/CODS, 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)

On Learning Ising Models under Huber's Contamination Model. - Adarsh Prasad; Vishwak Srinivasan; Sivaraman Balakrishnan; Pradeep Ravikumar (NeurIPS, 2020)

Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. - Dhruv Malik; Aldo Pacchiano; Vishwak Srinivasan; Yuanzhi Li (ICML, 2021)

Subseasonal climate prediction in the western US using Bayesian spatial models. - Vishwak Srinivasan; Justin Khim; Arindam Banerjee 001; Pradeep Ravikumar (UAI, 2021)

Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm. - Vishwak Srinivasan; Andre Wibisono; Ashia C. Wilson (COLT, 2024)

High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm. - Vishwak Srinivasan; Andre Wibisono; Ashia Wilson (ALT, 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, 2025)