| 2025 | ICML | Polynomial-Time Approximability of Constrained Reinforcement Learning. | Jeremy McMahan |
| 2025 | ICML | Anytime-Constrained Equilibria in Polynomial Time. | Jeremy McMahan |
| 2024 | AAAI | Optimal Attack and Defense for Reinforcement Learning. | Jeremy McMahan, Young Wu, Xiaojin Zhu, Qiaomin Xie |
| 2024 | AAAI | Data Poisoning to Fake a Nash Equilibria for Markov Games. | Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie |
| 2024 | AISTATS | Anytime-Constrained Reinforcement Learning. | Jeremy McMahan, Xiaojin Zhu |
| 2024 | CogSci | Various Misleading Visual Features in Misleading Graphs: Do they truly deceive us? | Jihyun Rho, Martina A. Rau, Shubham Kumar Bharti, Rosanne Luu, Jeremy McMahan, Andrew Wang, Jerry Zhu |
| 2024 | ICML | Roping in Uncertainty: Robustness and Regularization in Markov Games. | Jeremy McMahan, Giovanni Artiglio, Qiaomin Xie |
| 2024 | ICML | Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value. | Young Wu, Jeremy McMahan, Yiding Chen, Yudong Chen, Jerry Zhu, Qiaomin Xie |
| 2023 | AAAI | Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning. | Young Wu, Jeremy McMahan, Xiaojin Zhu, Qiaomin Xie |