Manuscripts:
- Conditional Flow Matching for Transport Between Markov Processes (2026)
arXiv preprint
with Syamantak Kumar, Saptarshi Roy and Purnamrita Sarkar
Conference Publications:
- Fine-Tuning Diffusion Models via Intermediate Distribution Shaping (2025)
ICLR (2026), to appear
with Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen, Karthikeyan Shanmugam and Sanjay Shakkottai - Poisson Midpoint Method for Log Concave Sampling: Beyond the Strong Error Lower Bounds (2025)
ICLR (2026), to appear
with Rishikesh Srinivasan - Dimension-free Score Matching and Time Bootstrapping for Diffusion Models (2025)
NeurIPS (2025)
with Syamantak Kumar and Puranmrita Sarkar - Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization (2025)
COLT 2025 (to appear)
with Anant Raj and Chandan Tankala - Glauber Generative Model: Discrete Diffusion Models via Binary Classification (2024)
ICLR 2025
with Harshit Varma and Karthikeyan Shanmugam - The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient Discretization for Diffusion Models (2024)
NeurIPS 2024
with Saravanan Kandasamy - Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD (2024)
NeurIPS 2024
with Aniket Das, Soumyabrata Pal, Arun Suggala, Prateek Varshney - A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health (2024)
NeurIPS 2024
with Nikhil Behari, Edwin Zhang, Yunfan Zhao, Aparna Taneja and Milind Tambe - Stochastic re-weighted gradient descent via distributionally robust optimization (2023)
TMLR (to appear, 2024+)
with Ramnath Kumar, Kushal Majmundar and Arun Suggala - Towards Zero Shot Learning in Restless Multi-armed Bandits (2023)
IJCAI 2024
with Yunfan Zhao, Nikhil Behari, Edward Hughes, Edwin Zhang, Karl Tuyls, Aparna Taneja and Milind Tambe
- Look Back When Surprised: Stabilizing Reverse Experience Replay for Neural Approximation (2022)
TMLR 2024
arXiv preprint
with Ramnath Kumar - Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation (2023)
NeurIPS 2023 [Spotlight]
with Aniket Das - Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms
COLT 2023
with Aniket Das and Anant Raj - Near Optimal Heteroscedastic Regression with Symbiotic Learning
COLT 2023
with Dheeraj Baby, Aniket Das and Praneeth Netrapalli - Multi-User Reinforcement Learning with Low Rank Rewards
ICML 2023
with Naman Agarwal, Prateek Jain, Suhas Kowshik and Praneeth Netrapalli - Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
AISTATS 2023
with Gandharv Patil, L A Prashanth and Doina Precup - Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Bandits
AAMAS 2023
with Abheek Ghosh, Manish Jain and Milind Tambe - Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
ICLR 2022
with Naman Agarwal, Syomantak Chaudhuri, Prateek Jain and Praneeth Netrapalli.
- Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. (2021)
NeurIPS 2021 (spotlight)
with Prateek Jain, Suhas Kowshik and Praneeth Netrapalli. - Streaming Linear System Identification with Reverse Experience Replay (2021)
NeurIPS 2021
with Prateek Jain, Suhas Kowshik and Praneeth Netrapalli.
- The staircase property: How hierarchical structure can guide deep learning. (2021)
NeurIPS 2021
with Emmanuel Abbe, Enric Boix-Adsera, Matthew Brennan and Guy Bresler - A Law of Robustness for Two-Layers Neural Networks. (2020)
COLT 2021
with Sébastien Bubeck and Yuanzhi Li - Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth. (2020)
NeurIPS 2020
with Guy Bresler.
- Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. (2020)
NeurIPS 2020 (spotlight)
with Guy Bresler, Prateek Jain, Praneeth Netrapalli, Xian Wu
- A Corrective View of Neural Networks: Representation, Memorization and Learning. (2020)
COLT 2020
arXiv preprint : arxiv:2002.00274
with Guy Bresler. - SGD without Replacement: Sharper Rates for General Smooth Convex Functions. (2019)
ICML 2019
with Praneeth Netrapalli and Prateek Jain - Optimal Single Sample Tests for Structured versus Unstructured Network Data. (2018)
COLT 2018.
arXiv preprint : arxiv:1802.06186
with Guy Bresler
Journal Publications:
- Metastable Mixing of Markov Chains: Efficiently Sampling Low Temperature Exponential Random Graphs (2024)
Annals of Applied Probability
arXiv preprint
with Guy Bresler and Eshaan Nichani
- Making Last Iterate of Stochastic Gradient Descent Information Theoretically Optimal. (2021)
SIAM Journal on Optimization (2021) , COLT 2019 (extended abstract)
with Praneeth Netrapalli and Prateek Jain - Phase Transition for Detecting Latent Geometry in Random Graphs. (2020)
Probability Theory and Related Fields
arXiv preprint: arxiv:1910.14167
with Matthew Brennan and Guy Bresler - Stein’s Method for Stationary Distributions of Markov Chains and Application to Ising Models. (2019)
Annals of Applied Probability
arXiv preprint: arxiv:1712.05743,
with Guy Bresler - Continuous limit of discrete quantum walks. (2015)
Physical Review A, arxiv:1501.06950
with Todd A. Brun
Other Manuscripts:
- Interleaved Gibbs Diffusion for Constrained Generation (2025)
arXiv preprint
with Gautham Govind Anil, Sachin Yadav, Karthikeyan Shanmugam and Prateek Jain