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Eric Mazumdar
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- affiliation: California Institute of Technology, Pasadena, CA, USA
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2020 – today
- 2025
- [i34]Shangding Gu, Laixi Shi, Muning Wen, Ming Jin, Eric Mazumdar, Yuejie Chi, Adam Wierman, Costas J. Spanos:
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning. CoRR abs/2502.19652 (2025) - [i33]Yizhou Zhang, Yi-An Ma, Eric Mazumdar:
Learning to Steer Learners in Games. CoRR abs/2502.20770 (2025) - 2024
- [c32]Chinmay Maheshwari, James Cheng, Shankar Sastry, Lillian J. Ratliff, Eric Mazumdar:
Follower Agnostic Learning in Stackelberg Games. CDC 2024: 222-228 - [c31]Lauren E. Conger, Yiheng Lin, Adam Wierman, Eric Mazumdar:
Characterizing Controllability and Observability for Systems with Locality, Communication, and Actuation Constraints. CDC 2024: 8491-8497 - [c30]Kishan Panaganti, Adam Wierman, Eric Mazumdar:
Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data. ICML 2024 - [c29]Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman:
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty. ICML 2024 - [c28]Zaiwei Chen, Eric Mazumdar:
Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities. NeurIPS 2024 - [c27]Pierre Clavier, Laixi Shi, Erwan Le Pennec, Eric Mazumdar, Adam Wierman, Matthieu Geist:
Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms. NeurIPS 2024 - [c26]Tinashe Handina, Eric Mazumdar:
Understanding Model Selection for Learning in Strategic Environments. NeurIPS 2024 - [c25]Zaiwei Chen
, Kaiqing Zhang
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. EC 2024: 378 - [i32]Tinashe Handina, Eric Mazumdar:
Rethinking Scaling Laws for Learning in Strategic Environments. CoRR abs/2402.07588 (2024) - [i31]Josefine B. Graebener, Apurva S. Badithela, Denizalp Goktas, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray:
Flow-Based Synthesis of Reactive Tests for Discrete Decision-Making Systems with Temporal Logic Specifications. CoRR abs/2404.09888 (2024) - [i30]Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman:
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty. CoRR abs/2404.18909 (2024) - [i29]Kishan Panaganti, Adam Wierman, Eric Mazumdar:
Model-Free Robust φ-Divergence Reinforcement Learning Using Both Offline and Online Data. CoRR abs/2405.05468 (2024) - [i28]Eric Mazumdar, Kishan Panaganti, Laixi Shi:
Tractable Equilibrium Computation in Markov Games through Risk Aversion. CoRR abs/2406.14156 (2024) - [i27]Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman:
Last-Iterate Convergence of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. CoRR abs/2409.01447 (2024) - [i26]Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman:
Can We Break the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning? CoRR abs/2409.20067 (2024) - 2023
- [c24]Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic:
Algorithmic Collective Action in Machine Learning. ICML 2023: 12570-12586 - [c23]Apurva Badithela, Josefine B. Graebener, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray:
Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows. ICRA 2023: 12430-12436 - [c22]Lauren E. Conger, Sydney Vernon, Eric Mazumdar:
Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees. L4DC 2023: 420-430 - [c21]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 - [c20]Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff:
Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows. NeurIPS 2023 - [i25]Chinmay Maheshwari, S. Shankar Sastry, Lillian J. Ratliff, Eric Mazumdar:
Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games. CoRR abs/2302.01421 (2023) - [i24]Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic:
Algorithmic Collective Action in Machine Learning. CoRR abs/2302.04262 (2023) - [i23]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 abs/2303.03100 (2023) - [i22]Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff:
Coupled Gradient Flows for Strategic Non-Local Distribution Shift. CoRR abs/2307.01166 (2023) - [i21]Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman:
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. CoRR abs/2312.04905 (2023) - 2022
- [c19]Yaodong Yu, Tianyi Lin, Eric V. Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. AISTATS 2022: 1219-1250 - [c18]Chinmay Maheshwari
Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. AISTATS 2022: 6702-6734 - [c17]Lauren E. Conger, Jing Shuang Lisa Li
Nonlinear System Level Synthesis for Polynomial Dynamical Systems. CDC 2022: 3846-3852 - [c16]Pan Xu, Hongkai Zheng, Eric V. Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar:
Langevin Monte Carlo for Contextual Bandits. ICML 2022: 24830-24850 - [c15]Chinmay Maheshwari, Shankar Sastry, Eric Mazumdar:
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. NeurIPS 2022 - [i20]Chinmay Maheshwari, Eric Mazumdar, Shankar Sastry:
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. CoRR abs/2206.02344 (2022) - [i19]Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar:
Langevin Monte Carlo for Contextual Bandits. CoRR abs/2206.11254 (2022) - [i18]Tijana Zrnic, Eric Mazumdar:
A Note on Zeroth-Order Optimization on the Simplex. CoRR abs/2208.01185 (2022) - [i17]Apurva Badithela, Josefine B. Graebener, Wyatt Ubellacker, Eric V. Mazumdar, Aaron D. Ames, Richard M. Murray:
Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows. CoRR abs/2210.10304 (2022) - 2021
- [c14]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? NeurIPS 2021: 15257-15269 - [c13]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: 29049-29063 - [i16]Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan:
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR abs/2104.13326 (2021) - [i15]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 abs/2106.09082 (2021) - [i14]Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan:
Who Leads and Who Follows in Strategic Classification? CoRR abs/2106.12529 (2021) - 2020
- [j2]Eric Mazumdar
On Gradient-Based Learning in Continuous Games. SIAM J. Math. Data Sci. 2(1): 103-131 (2020) - [j1]Lillian J. Ratliff
Inverse Risk-Sensitive Reinforcement Learning. IEEE Trans. Autom. Control. 65(3): 1256-1263 (2020) - [c12]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: 860-868 - [c11]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: 118-125 - [c10]Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry:
Expert Selection in High-Dimensional Markov Decision Processes. CDC 2020: 3604-3610 - [c9]Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry:
High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC 2020: 4275-4280 - [c8]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett:
On Approximate Thompson Sampling with Langevin Algorithms. ICML 2020: 6797-6807 - [c7]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: 1364-1371 - [i13]Eric Mazumdar, Lillian J. Ratliff:
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. CoRR abs/2002.01007 (2020) - [i12]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On Thompson Sampling with Langevin Algorithms. CoRR abs/2002.10002 (2020) - [i11]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 abs/2004.02766 (2020) - [i10]Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry:
Expert Selection in High-Dimensional Markov Decision Processes. CoRR abs/2010.15599 (2020)
2010 – 2019
- 2019
- [c6]Eric Mazumdar, Lillian J. Ratliff
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. CDC 2019: 6899-6904 - [c5]Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel Burden:
Convergence Analysis of Gradient-Based Learning in Continuous Games. UAI 2019: 935-944 - [i9]Eric Mazumdar, Michael I. Jordan, S. Shankar Sastry:
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games. CoRR abs/1901.00838 (2019) - [i8]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 abs/1906.00731 (2019) - [i7]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 abs/1907.03712 (2019) - [i6]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 abs/1910.13272 (2019) - 2018
- [c4]Margaret P. Chapman, Eric V. Mazumdar, Ellen M. Langer, Rosalie C. Sears, Claire J. Tomlin:
On the Analysis of Cyclic Drug Schedules for Cancer Treatment using Switched Dynamical Systems. CDC 2018: 3503-3509 - [i5]Eric Mazumdar, Lillian J. Ratliff:
On the Convergence of Competitive, Multi-Agent Gradient-Based Learning. CoRR abs/1804.05464 (2018) - 2017
- [c3]Eric Mazumdar, Lillian J. Ratliff
Gradient-based inverse risk-sensitive reinforcement learning. CDC 2017: 5796-5801 - [i4]Roy Dong, Eric Mazumdar, S. Shankar Sastry:
Optimal Causal Imputation for Control. CoRR abs/1703.07049 (2017) - [i3]Lillian J. Ratliff, Eric Mazumdar:
Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods. CoRR abs/1703.09842 (2017) - [i2]Eric Mazumdar, Roy Dong, Vicenc Rubies-Royo, Claire J. Tomlin, S. Shankar Sastry:
A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes. CoRR abs/1707.05714 (2017) - 2016
- [c2]Lillian J. Ratliff
To observe or not to observe: Queuing game framework for urban parking. CDC 2016: 5286-5291 - [c1]Daniel J. Calderone, Eric Mazumdar, Lillian J. Ratliff
Understanding the impact of parking on urban mobility via routing games on queue-flow networks. CDC 2016: 7605-7610 - [i1]Lillian J. Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang:
To Observe or Not to Observe: Queuing Game Framework for Urban Parking. CoRR abs/1603.08995 (2016)
Coauthor Index
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