Dheeraj Mysore Nagaraj

Dheeraj Mysore Nagaraj

I am a Research Scientist at Google AI, Bangalore, India in the machine learning and optimization (MLO) team. I work on various topics in theoretical machine learning, applied probability and statistics. My current work focuses on designing learning algorithms for data with temporal dependence, random graphs and stochastic optimization algorithms. I recently completed my PhD at Lab for Information and Decision Systems (LIDS) at MIT advised by Prof. Guy Bresler. Prior to that I was an undergraduate student at IIT Madras.

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Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms
Aniket Das
Dheeraj Nagaraj
Anant Raj
Conference on Learning Theory 2023 (2023)
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Aniket Das
Dheeraj Nagaraj
NeurIPS 2023 (2023) (to appear)
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Aniket Das
Dheeraj Baby
Dheeraj Nagaraj
Praneeth Netrapalli
Conference on Learning Theory (COLT) (2023)
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularization
Dheeraj Nagaraj
Doina Precup
Gandharv Patil
Prashanth L A
AISTATS (2023) (to appear)
Indexibility is not Enough for Whittle: Improved Near-Optimal Algorithms for Restless Bandits
Abheek Ghosh
Dheeraj Nagaraj
Manish Jain
Milind Tambe
Autonomous Agents and Multi Agent Systems (AAMAS) (2023)
Metastable Mixing of Markov Chains: Efficiently Sampling Low Temperature Exponential Random Graphs
Guy Bresler
Dheeraj Nagaraj
Eshaan Nichani
Annals of Applied Probability (AAP) (2023) (to appear)
Multi User Reinforcement Learning with Low Rank Rewards
Naman Agarwal
Prateek Jain
Suhas Kowshik
Dheeraj Nagaraj
Praneeth Netrapalli
ICML (2023)
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