Controlling G-AIMD by index policy. - Konstantin E. Avrachenkov; Vivek S. Borkar; Sarath Pattathil (CDC, 2017)

Massive machine-type communication (mMTC) access with integrated authentication. - Nuno K. Pratas; Sarath Pattathil; Cedomir Stefanovic; Petar Popovski (ICC, 2017)

Optimal distributed scheduling for single-hop wireless networks. - Sarath Pattathil; Jayakrishnan Nair 001 (WiOpt, 2017)

Distributed Server Allocation for Content Delivery Networks. - Sarath Pattathil; Vivek S. Borkar; Gaurav S. Kasbekar (CoRR, 2017)

Concentration bounds for two time scale stochastic approximation. - Vivek S. Borkar; Sarath Pattathil (Allerton, 2018)

Persistence of the Jordan Center in Random Growing Trees. - Sarath Pattathil; Nikhil Karamchandani; Dhruti Shah (ASONAM, 2018)

Opportunistic Scheduling as Restless Bandits. - Vivek S. Borkar; Gaurav S. Kasbekar; Sarath Pattathil; Priyesh Y. Shetty (IEEE Trans. Control. Netw. Syst., 2018)

Proximal Point Approximations Achieving a Convergence Rate of O(1/k) for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods. - Aryan Mokhtari; Asuman E. Ozdaglar; Sarath Pattathil (CoRR, 2019)

A Decentralized Proximal Point-type Method for Saddle Point Problems. - Weijie Liu 006; Aryan Mokhtari; Asuman E. Ozdaglar; Sarath Pattathil; Zebang Shen; Nenggan Zheng (CoRR, 2019)

A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach. - Aryan Mokhtari; Asuman E. Ozdaglar; Sarath Pattathil (AISTATS, 2020)

An Optimal Multistage Stochastic Gradient Method for Minimax Problems. - Alireza Fallah 001; Asuman E. Ozdaglar; Sarath Pattathil (CDC, 2020)

Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems. - Noah Golowich; Sarath Pattathil; Constantinos Daskalakis; Asuman E. Ozdaglar (COLT, 2020)

Tight last-iterate convergence rates for no-regret learning in multi-player games. - Noah Golowich; Sarath Pattathil; Constantinos Daskalakis (NeurIPS, 2020)

Jordan centre in random trees: persistence and distance to root. - Sarath Pattathil; Nikhil Karamchandani; Dhruti Shah (J. Complex Networks, 2020)

Convergence Rate of 풪(1/k) for Optimistic Gradient and Extragradient Methods in Smooth Convex-Concave Saddle Point Problems. - Aryan Mokhtari; Asuman E. Ozdaglar; Sarath Pattathil (SIAM J. Optim., 2020)

What is a Good Metric to Study Generalization of Minimax Learners? - Asuman E. Ozdaglar; Sarath Pattathil; Jiawei Zhang 007; Kaiqing Zhang (NeurIPS, 2022)

Whittle indexability in egalitarian processor sharing systems. - Vivek S. Borkar; Sarath Pattathil (Ann. Oper. Res., 2022)

Symmetric (Optimistic) Natural Policy Gradient for Multi-Agent Learning with Parameter Convergence. - Sarath Pattathil; Kaiqing Zhang; Asuman E. Ozdaglar (AISTATS, 2023)

Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation. - Asuman E. Ozdaglar; Sarath Pattathil; Jiawei Zhang 007; Kaiqing Zhang (ICML, 2023)

Learning, Diversity and Adaptation in Changing Environments: The Role of Weak Links. - Daron Acemoglu; Asuman E. Ozdaglar; Sarath Pattathil (CoRR, 2023)

Optimization and Generalization of Minimax Algorithms - Sarath Pattathil (2023)

Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics. - Brendan Lucier; Sarath Pattathil; Aleksandrs Slivkins; Mengxiao Zhang (COLT, 2024)

Optimal adaptive testing for epidemic control: Combining molecular and serology tests. - Daron Acemoglu; Alireza Fallah 001; Andrea Giometto; Daniel P. Huttenlocher; Asuman E. Ozdaglar; Francesca Parise; Sarath Pattathil (Autom., 2024)