| 2024 | ICML | Hybrid Reinforcement Learning from Offline Observation Alone. | Yuda Song, Drew Bagnell, Aarti Singh |
| 2024 | ICML | Hybrid Inverse Reinforcement Learning. | Juntao Ren, Gokul Swamy, Steven Wu, Drew Bagnell, Sanjiban Choudhury |
| 2023 | ICLR | Hybrid RL: Using both offline and online data can make RL efficient. | Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun |
| 2023 | ICML | Inverse Reinforcement Learning without Reinforcement Learning. | Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu |
| 2023 | ICML | The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms. | Anirudh Vemula, Yuda Song, Aarti Singh, Drew Bagnell, Sanjiban Choudhury |
| 2022 | ICML | Causal Imitation Learning under Temporally Correlated Noise. | Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu |
| 2019 | ICML | Provably Efficient Imitation Learning from Observation Alone. | Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell |
| 2014 | AISTATS | Near Optimal Bayesian Active Learning for Decision Making. | Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha S. Srinivasa |
| 2013 | ICML | Learning Policies for Contextual Submodular Prediction. | Stphane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, Drew Bagnell |
| 2012 | ICML | Agnostic System Identification for Model-Based Reinforcement Learning. | Stphane Ross, Drew Bagnell |
| 2011 | ICML | Generalized Boosting Algorithms for Convex Optimization. | Alexander Grubb, Drew Bagnell |
| 2011 | ICML | Computational Rationalization: The Inverse Equilibrium Problem. | Kevin Waugh, Brian D. Ziebart, Drew Bagnell |
| 2010 | AAAI | Maximum Causal Entropy Correlated Equilibria for Markov Games. | Brian D. Ziebart, Drew Bagnell, Anind K. Dey |
| 2005 | ICRA | Learning Opportunity Costs in Multi-Robot Market Based Planners. | Jeff G. Schneider, David Apfelbaum, Drew Bagnell, Reid G. Simmons |