| 2025 | AISTATS | Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data. | Chengrui Qu, Laixi Shi, Kishan Panaganti, Pengcheng You, Adam Wierman |
| 2025 | ICLR | Tractable Multi-Agent Reinforcement Learning through Behavioral Economics. | Eric Mazumdar, Kishan Panaganti, Laixi Shi |
| 2025 | ICML | Online Robust Reinforcement Learning Through Monte-Carlo Planning. | Tuan Dam, Kishan Panaganti, Brahim Driss, Adam Wierman |
| 2024 | ICML | Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data. | Kishan Panaganti, Adam Wierman, Eric Mazumdar |
| 2023 | AISTATS | Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning. | Zaiyan Xu, Kishan Panaganti, Dileep Kalathil |
| 2023 | ICLR | Personalized Reward Learning with Interaction-Grounded Learning (IGL). | Jessica Maghakian, Paul Mineiro, Kishan Panaganti, Mark Rucker, Akanksha Saran, Cheng Tan |
| 2022 | AISTATS | Sample Complexity of Robust Reinforcement Learning with a Generative Model. | Kishan Panaganti, Dileep M. Kalathil |
| 2022 | RecSys | Interaction-Grounded Learning for Recommender Systems. | Jessica Maghakian, Kishan Panaganti, Paul Mineiro, Akanksha Saran, Cheng Tan |