| A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach A Mokhtari, A Ozdaglar, S Pattathil International Conference on Artificial Intelligence and Statistics, 1497-1507, 2020 | 362 | 2020 |
| Convergence Rate of for Optimistic Gradient and Extragradient Methods in Smooth Convex-Concave Saddle Point Problems A Mokhtari, AE Ozdaglar, S Pattathil SIAM Journal on Optimization 30 (4), 3230-3251, 2020 | 156* | 2020 |
| Last iterate is slower than averaged iterate in smooth convex-concave saddle point problems N Golowich, S Pattathil, C Daskalakis, A Ozdaglar Conference on Learning Theory, 1758-1784, 2020 | 110 | 2020 |
| Tight last-iterate convergence rates for no-regret learning in multi-player games N Golowich, S Pattathil, C Daskalakis 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 91 | 2020 |
| Opportunistic scheduling as restless bandits VS Borkar, GS Kasbekar, S Pattathil, PY Shetty IEEE Transactions on Control of Network Systems 5 (4), 1952-1961, 2017 | 45 | 2017 |
| An optimal multistage stochastic gradient method for minimax problems A Fallah, A Ozdaglar, S Pattathil 2020 59th IEEE Conference on Decision and Control (CDC), 3573-3579, 2020 | 37 | 2020 |
| Concentration bounds for two time scale stochastic approximation VS Borkar, S Pattathil 2018 56th Annual Allerton Conference on Communication, Control, and …, 2018 | 33 | 2018 |
| A Decentralized Proximal Point-type Method for Non-convex Non-concave Saddle Point Problems W Liu, A Mokhtari, A Ozdaglar, S Pattathil, Z Shen, N Zheng 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 32* | 2020 |
| Massive machine-type communication (mMTC) access with integrated authentication NK Pratas, S Pattathil, Č Stefanović, P Popovski 2017 IEEE International Conference on Communications (ICC), 1-6, 2017 | 29 | 2017 |
| Whittle indexability in egalitarian processor sharing systems VS Borkar, S Pattathil Annals of Operations Research 317 (2), 417-437, 2022 | 23 | 2022 |
| Revisiting the linear-programming framework for offline rl with general function approximation AE Ozdaglar, S Pattathil, J Zhang, K Zhang International Conference on Machine Learning, 26769-26791, 2023 | 22 | 2023 |
| Optimal adaptive testing for epidemic control: combining molecular and serology tests D Acemoglu, A Fallah, A Giometto, D Huttenlocher, A Ozdaglar, F Parise, ... Automatica 160, 111391, 2024 | 21 | 2024 |
| Autobidders with budget and roi constraints: Efficiency, regret, and pacing dynamics B Lucier, S Pattathil, A Slivkins, M Zhang The Thirty Seventh Annual Conference on Learning Theory, 3642-3643, 2024 | 20 | 2024 |
| Symmetric (optimistic) natural policy gradient for multi-agent learning with parameter convergence S Pattathil, K Zhang, A Ozdaglar International Conference on Artificial Intelligence and Statistics, 5641-5685, 2023 | 14 | 2023 |
| What is a good metric to study generalization of minimax learners? A Ozdaglar, S Pattathil, J Zhang, K Zhang Advances in Neural Information Processing Systems 35, 38190-38203, 2022 | 12 | 2022 |
| Controlling G-AIMD by index policy KE Avrachenkov, VS Borkar, S Pattathil 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 120-125, 2017 | 7 | 2017 |
| Learning, diversity and adaptation in changing environments: The role of weak links D Acemoglu, A Ozdaglar, S Pattathil National Bureau of Economic Research, 2023 | 2 | 2023 |
| Distributed server allocation for content delivery networks S Pattathil, VS Borkar, GS Kasbekar Queueing Models and Service Management 2 (2), 2017 | 2 | 2017 |
| Persistence of the Jordan center in random growing trees S Pattathil, N Karamchandani, D Shah 2018 IEEE/ACM International Conference on Advances in Social Networks …, 2018 | 1 | 2018 |
| Optimization and Generalization of Minimax Algorithms S Pattathil Massachusetts Institute of Technology, 2023 | | 2023 |
Asuman OzdaglarMathworks Professor, EECS, MITVerified email at mit.edu
