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 (CDC, 2016)

To observe or not to observe: Queuing game framework for urban parking. - Lillian J. Ratliff; Chase Dowling; Eric Mazumdar; Baosen Zhang (CDC, 2016)

Gradient-based inverse risk-sensitive reinforcement learning. - Eric Mazumdar; Lillian J. Ratliff; Tanner Fiez; S. Shankar Sastry (CDC, 2017)

Optimal Causal Imputation for Control. - Roy Dong; Eric Mazumdar; S. Shankar Sastry (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)

Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods. - Lillian J. Ratliff; Eric Mazumdar (CoRR, 2017)

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 (CDC, 2018)

On the Convergence of Competitive, Multi-Agent Gradient-Based Learning. - Eric Mazumdar; Lillian J. Ratliff (CoRR, 2018)

Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. - Eric Mazumdar; Lillian J. Ratliff (CDC, 2019)

Convergence Analysis of Gradient-Based Learning in Continuous Games. - Benjamin Chasnov; Lillian J. Ratliff; Eric Mazumdar; Samuel Burden (UAI, 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 with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings. - Benjamin Chasnov; Lillian J. Ratliff; Eric Mazumdar; Samuel A. Burden (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)

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 Linear Quadratic Games. - Eric Mazumdar; Lillian J. Ratliff; Michael I. Jordan; S. Shankar Sastry (AAMAS, 2020)

High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. - Eric Mazumdar; Tyler Westenbroek; Michael I. Jordan; S. Shankar Sastry (CDC, 2020)

Expert Selection in High-Dimensional Markov Decision Processes. - Vicenc Rubies-Royo; Eric Mazumdar; Roy Dong; Claire J. Tomlin; S. Shankar Sastry (CDC, 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 (CDC, 2020)

On Approximate Thompson Sampling with Langevin Algorithms. - Eric Mazumdar; Aldo Pacchiano; Yi-An Ma; Michael I. Jordan; Peter L. Bartlett (ICML, 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 (ICRA, 2020)

On Thompson Sampling with Langevin Algorithms. - Eric Mazumdar; Aldo Pacchiano; Yi-An Ma; Peter L. Bartlett; Michael I. Jordan (CoRR, 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 Gradient-Based Learning in Continuous Games. - Eric Mazumdar; Lillian J. Ratliff; S. Shankar Sastry (SIAM J. Math. Data Sci., 2020)

Inverse Risk-Sensitive Reinforcement Learning. - Lillian J. Ratliff; Eric Mazumdar (IEEE Trans. Autom. Control., 2020)

Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games. - Tanner Fiez; Lillian J. Ratliff; Eric Mazumdar; Evan Faulkner; Adhyyan Narang (NeurIPS, 2021)

Who Leads and Who Follows in Strategic Classification? - Tijana Zrnic; Eric Mazumdar; S. Shankar Sastry; Michael I. Jordan (NeurIPS, 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)

Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. - Chinmay Maheshwari; Chih-Yuan Chiu; Eric Mazumdar; Shankar Sastry 001; Lillian J. Ratliff (AISTATS, 2022)

Fast Distributionally Robust Learning with Variance-Reduced Min-Max Optimization. - Yaodong Yu; Tianyi Lin; Eric V. Mazumdar; Michael I. Jordan (AISTATS, 2022)

Nonlinear System Level Synthesis for Polynomial Dynamical Systems. - Lauren E. Conger; Jing Shuang Lisa Li; Eric Mazumdar; Steven L. Brunton (CDC, 2022)

Langevin Monte Carlo for Contextual Bandits. - Pan Xu 002; Hongkai Zheng; Eric V. Mazumdar; Kamyar Azizzadenesheli; Animashree Anandkumar (ICML, 2022)

Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. - Chinmay Maheshwari; Shankar Sastry 001; Eric Mazumdar (NeurIPS, 2022)

A Note on Zeroth-Order Optimization on the Simplex. - Tijana Zrnic; Eric Mazumdar (CoRR, 2022)

Algorithmic Collective Action in Machine Learning. - Moritz Hardt; Eric Mazumdar; Celestine Mendler-Dünner; Tijana Zrnic (ICML, 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 (ICRA, 2023)

Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees. - Lauren E. Conger; Sydney Vernon; Eric Mazumdar (L4DC, 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 (NeurIPS, 2023)

Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows. - Lauren E. Conger; Franca Hoffmann; Eric Mazumdar; Lillian J. Ratliff (NeurIPS, 2023)

Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games. - Chinmay Maheshwari; S. Shankar Sastry; Lillian J. Ratliff; Eric Mazumdar (CoRR, 2023)

Coupled Gradient Flows for Strategic Non-Local Distribution Shift. - Lauren E. Conger; Franca Hoffmann; Eric Mazumdar; Lillian J. Ratliff (CoRR, 2023)

Characterizing Controllability and Observability for Systems with Locality, Communication, and Actuation Constraints. - Lauren E. Conger; Yiheng Lin 001; Adam Wierman; Eric Mazumdar (CDC, 2024)

Follower Agnostic Learning in Stackelberg Games. - Chinmay Maheshwari; James Cheng; Shankar Sastry 001; Lillian J. Ratliff; Eric Mazumdar (CDC, 2024)

Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data. - Kishan Panaganti; Adam Wierman; Eric Mazumdar (ICML, 2024)

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty. - Laixi Shi; Eric Mazumdar; Yuejie Chi; Adam Wierman (ICML, 2024)

Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities. - Zaiwei Chen; Eric Mazumdar (NeurIPS, 2024)

Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms. - Pierre Clavier; Laixi Shi; Erwan Le Pennec; Eric Mazumdar; Adam Wierman; Matthieu Geist (NeurIPS, 2024)

Understanding Model Selection for Learning in Strategic Environments. - Tinashe Handina; Eric Mazumdar (NeurIPS, 2024)

Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. - Zaiwei Chen; Kaiqing Zhang; Eric Mazumdar; Asuman E. Ozdaglar; Adam Wierman (EC, 2024)

Rethinking Scaling Laws for Learning in Strategic Environments. - Tinashe Handina; Eric Mazumdar (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)

Tractable Equilibrium Computation in Markov Games through Risk Aversion. - Eric Mazumdar; Kishan Panaganti; Laixi Shi (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)

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)

Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning. - Shangding Gu; Laixi Shi; Muning Wen; Ming Jin 002; Eric Mazumdar; Yuejie Chi; Adam Wierman; Costas J. Spanos (ICLR, 2025)

Tractable Multi-Agent Reinforcement Learning through Behavioral Economics. - Eric Mazumdar; Kishan Panaganti; Laixi Shi (ICLR, 2025)

Learning to Steer Learners in Games. - Yizhou Zhang; Yi-An Ma; Eric Mazumdar (CoRR, 2025)

Convergent Q-Learning for Infinite-Horizon General-Sum Markov Games through Behavioral Economics. - Yizhou Zhang; Eric Mazumdar (CoRR, 2025)