Eric Mazumdar

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 49

Top Topics

Nonlinear Systems

Markov Decision Process

Stochastic Games

Nash Equilibria

Top Venues

Publications

Kishan Panaganti, Adam Wierman, Eric Mazumdar
Model-Free Robust φ-Divergence Reinforcement Learning Using Both Offline and Online Data. CoRR (2024)
Eric Mazumdar, Kishan Panaganti, Laixi Shi
Tractable Equilibrium Computation in Markov Games through Risk Aversion. CoRR (2024)
Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty. CoRR (2024)
Tinashe Handina, Eric Mazumdar
Rethinking Scaling Laws for Learning in Strategic Environments. CoRR (2024)
Lauren E. Conger, Sydney Vernon, Eric Mazumdar
Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees. L4DC (2023)
Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. NeurIPS (2023)
Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff
Coupled Gradient Flows for Strategic Non-Local Distribution Shift. CoRR (2023)
Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. CoRR (2023)
Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic
Algorithmic Collective Action in Machine Learning. ICML (2023)
Chinmay Maheshwari, S. Shankar Sastry, Lillian J. Ratliff, Eric Mazumdar
Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games. CoRR (2023)
Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. CoRR (2023)
Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff
Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows. NeurIPS (2023)
Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic
Algorithmic Collective Action in Machine Learning. CoRR (2023)
Chinmay Maheshwari, Shankar Sastry, Eric Mazumdar
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. NeurIPS (2022)
Chinmay Maheshwari, Eric Mazumdar, Shankar Sastry
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. CoRR (2022)
Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, Shankar Sastry, Lillian J. Ratliff
Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. AISTATS (2022)
Lauren Conger, Jing Shuang Lisa Li, Eric Mazumdar, Steven L. Brunton
Nonlinear System Level Synthesis for Polynomial Dynamical Systems. CDC (2022)
Tijana Zrnic, Eric Mazumdar
A Note on Zeroth-Order Optimization on the Simplex. CoRR (2022)
Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar
Langevin Monte Carlo for Contextual Bandits. CoRR (2022)
Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR (2021)
Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan
Who Leads and Who Follows in Strategic Classification? CoRR (2021)
Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, S. Shankar Sastry, Lillian J. Ratliff
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization. CoRR (2021)
Tanner Fiez, Lillian J. Ratliff, Eric Mazumdar, Evan Faulkner, Adhyyan Narang
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games. NeurIPS (2021)
Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan
Who Leads and Who Follows in Strategic Classification? NeurIPS (2021)
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
On Thompson Sampling with Langevin Algorithms. CoRR (2020)
Vicenç Rúbies Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry
Expert Selection in High-Dimensional Markov Decision Processes. CoRR (2020)
Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry
On Gradient-Based Learning in Continuous Games. SIAM J. Math. Data Sci. 2 (1) (2020)
Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry
High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC (2020)
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
On Approximate Thompson Sampling with Langevin Algorithms. ICML (2020)
Vicenç Rúbies Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry
Expert Selection in High-Dimensional Markov Decision Processes. CDC (2020)
Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry
Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning. CDC (2020)
Eric Mazumdar, Lillian J. Ratliff
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. CoRR (2020)
Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin
Feedback Linearization for Uncertain Systems via Reinforcement Learning. ICRA (2020)
Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning. CoRR (2020)
Lillian J. Ratliff, Eric Mazumdar
Inverse Risk-Sensitive Reinforcement Learning. IEEE Trans. Autom. Control. 65 (3) (2020)
Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. AAMAS (2020)
Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel Burden
Convergence Analysis of Gradient-Based Learning in Continuous Games. UAI (2019)
Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin
Feedback Linearization for Unknown Systems via Reinforcement Learning. CoRR (2019)
Eric Mazumdar, Lillian J. Ratliff
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. CDC (2019)
Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry
Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings. CoRR (2019)
Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel A. Burden
Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings. CoRR (2019)
Eric Mazumdar, Lillian J. Ratliff
On the Convergence of Competitive, Multi-Agent Gradient-Based Learning. CoRR (2018)
Eric Mazumdar, Lillian J. Ratliff, Tanner Fiez, S. Shankar Sastry
Gradient-based inverse risk-sensitive reinforcement learning. CDC (2017)
Eric Mazumdar, Roy Dong, Vicenç Rúbies Royo, Claire J. Tomlin, S. Shankar Sastry
A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes. CoRR (2017)
Roy Dong, Eric Mazumdar, S. Shankar Sastry
Optimal Causal Imputation for Control. CoRR (2017)
Lillian J. Ratliff, Eric Mazumdar
Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods. CoRR (2017)
Lillian J. Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang
To observe or not to observe: Queuing game framework for urban parking. CDC (2016)
Lillian J. Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang
To Observe or Not to Observe: Queuing Game Framework for Urban Parking. CoRR (2016)
Daniel J. Calderone, Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry
Understanding the impact of parking on urban mobility via routing games on queue-flow networks. CDC (2016)