Eric Mazumdar

According to our database, Eric Mazumdar authored at least 48 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Can We Break the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning?

Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman

CoRR, 2024

Last-Iterate Convergence of Payoff-Based Independent Learning in Zero-Sum Stochastic Games.

Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman

CoRR, 2024

Tractable Equilibrium Computation in Markov Games through Risk Aversion.

Eric Mazumdar, Kishan Panaganti, Laixi Shi

CoRR, 2024

Flow-Based Synthesis of Reactive Tests for Discrete Decision-Making Systems with Temporal Logic Specifications.

Josefine B. Graebener, Apurva S. Badithela, Denizalp Goktas, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray

CoRR, 2024

Rethinking Scaling Laws for Learning in Strategic Environments.

Tinashe Handina, Eric Mazumdar

CoRR, 2024

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty.

Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data.

Kishan Panaganti, Adam Wierman, Eric Mazumdar

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games.

Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman

CoRR, 2023

Coupled Gradient Flows for Strategic Non-Local Distribution Shift.

Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff

CoRR, 2023

Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games.

Chinmay Maheshwari, S. Shankar Sastry, Lillian J. Ratliff, Eric Mazumdar

CoRR, 2023

Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows.

Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games.

Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees.

Lauren E. Conger, Sydney Vernon, Eric Mazumdar

Proceedings of the Learning for Dynamics and Control Conference, 2023

Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows.

Apurva Badithela, Josefine B. Graebener, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Algorithmic Collective Action in Machine Learning.

Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic

Proceedings of the International Conference on Machine Learning, 2023

2022

A Note on Zeroth-Order Optimization on the Simplex.

Tijana Zrnic, Eric Mazumdar

CoRR, 2022

Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets.

Chinmay Maheshwari, Shankar Sastry, Eric Mazumdar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Langevin Monte Carlo for Contextual Bandits.

Pan Xu, Hongkai Zheng, Eric V. Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar

Proceedings of the International Conference on Machine Learning, 2022

Nonlinear System Level Synthesis for Polynomial Dynamical Systems.

Lauren E. Conger, Jing Shuang Lisa Li, Eric Mazumdar, Steven L. Brunton

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization.

Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization.

Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, Shankar Sastry, Lillian J. Ratliff

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization.

Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, S. Shankar Sastry, Lillian J. Ratliff

CoRR, 2021

Who Leads and Who Follows in Strategic Classification?

Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games.

Tanner Fiez, Lillian J. Ratliff, Eric Mazumdar, Evan Faulkner, Adhyyan Narang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Inverse Risk-Sensitive Reinforcement Learning.

Lillian J. Ratliff, Eric Mazumdar

IEEE Trans. Autom. Control., 2020

On Gradient-Based Learning in Continuous Games.

Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry

SIAM J. Math. Data Sci., 2020

Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning.

Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry

CoRR, 2020

On Thompson Sampling with Langevin Algorithms.

Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan

CoRR, 2020

Feedback Linearization for Uncertain Systems via Reinforcement Learning.

Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin

Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

On Approximate Thompson Sampling with Langevin Algorithms.

Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett

Proceedings of the 37th International Conference on Machine Learning, 2020

Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning.

Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Expert Selection in High-Dimensional Markov Decision Processes.

Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems.

Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games.

Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019

Feedback Linearization for Unknown Systems via Reinforcement Learning.

Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin

CoRR, 2019

Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings.

Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry

CoRR, 2019

Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings.

Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel A. Burden

CoRR, 2019

On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games.

Eric Mazumdar, Michael I. Jordan, S. Shankar Sastry

CoRR, 2019

Convergence Analysis of Gradient-Based Learning in Continuous Games.

Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel Burden

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games.

Eric Mazumdar, Lillian J. Ratliff

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018

On the Convergence of Competitive, Multi-Agent Gradient-Based Learning.

Eric Mazumdar, Lillian J. Ratliff

CoRR, 2018

On the Analysis of Cyclic Drug Schedules for Cancer Treatment using Switched Dynamical Systems.

Margaret P. Chapman, Eric V. Mazumdar, Ellen M. Langer, Rosalie C. Sears, Claire J. Tomlin

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods.

Lillian J. Ratliff, Eric Mazumdar

CoRR, 2017

A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes.

Eric Mazumdar, Roy Dong, Vicenc Rubies-Royo, Claire J. Tomlin, S. Shankar Sastry

CoRR, 2017

Optimal Causal Imputation for Control.

Roy Dong, Eric Mazumdar, S. Shankar Sastry

CoRR, 2017

Gradient-based inverse risk-sensitive reinforcement learning.

Eric Mazumdar, Lillian J. Ratliff, Tanner Fiez, S. Shankar Sastry

Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

2016

To observe or not to observe: Queuing game framework for urban parking.

Lillian J. Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang

Proceedings of the 55th IEEE Conference on Decision and Control, 2016

Understanding the impact of parking on urban mobility via routing games on queue-flow networks.

Daniel J. Calderone, Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry

Proceedings of the 55th IEEE Conference on Decision and Control, 2016