Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap. - Gokul Swamy 001; Sanjiban Choudhury; J. Andrew Bagnell; Steven Wu 001 (ICML, 2021)

Of Moments and Matching: Trade-offs and Treatments in Imitation Learning. - Gokul Swamy 001; Sanjiban Choudhury; Zhiwei Steven Wu; J. Andrew Bagnell (CoRR, 2021)

A Critique of Strictly Batch Imitation Learning. - Gokul Swamy 001; Sanjiban Choudhury; J. Andrew Bagnell; Zhiwei Steven Wu (CoRR, 2021)

Causal Imitation Learning under Temporally Correlated Noise. - Gokul Swamy 001; Sanjiban Choudhury; Drew Bagnell; Steven Wu 001 (ICML, 2022)

Sequence Model Imitation Learning with Unobserved Contexts. - Gokul Swamy 001; Sanjiban Choudhury; J. Andrew Bagnell; Zhiwei Steven Wu (NeurIPS, 2022)

Minimax Optimal Online Imitation Learning via Replay Estimation. - Gokul Swamy 001; Nived Rajaraman; Matthew Peng; Sanjiban Choudhury; J. Andrew Bagnell; Steven Wu 001; Jiantao Jiao; Kannan Ramchandran (NeurIPS, 2022)

Game-Theoretic Algorithms for Conditional Moment Matching. - Gokul Swamy 001; Sanjiban Choudhury; J. Andrew Bagnell; Zhiwei Steven Wu (CoRR, 2022)

Inverse Reinforcement Learning without Reinforcement Learning. - Gokul Swamy 001; David Wu; Sanjiban Choudhury; Drew Bagnell; Zhiwei Steven Wu (ICML, 2023)

Learning Shared Safety Constraints from Multi-task Demonstrations. - Konwoo Kim; Gokul Swamy 001; Zuxin Liu; Ding Zhao; Sanjiban Choudhury; Zhiwei Steven Wu (NeurIPS, 2023)

Inverse Reinforcement Learning without Reinforcement Learning. - Gokul Swamy 001; Sanjiban Choudhury; J. Andrew Bagnell; Zhiwei Steven Wu (CoRR, 2023)

When is Transfer Learning Possible? - My Phan; Kianté Brantley; Stephanie Milani; Soroush Mehri; Gokul Swamy 001; Geoffrey J. Gordon (ICML, 2024)

Hybrid Inverse Reinforcement Learning. - Juntao Ren; Gokul Swamy 001; Steven Wu 001; Drew Bagnell; Sanjiban Choudhury (ICML, 2024)

A Minimaximalist Approach to Reinforcement Learning from Human Feedback. - Gokul Swamy 001; Christoph Dann; Rahul Kidambi; Steven Wu 001; Alekh Agarwal (ICML, 2024)

The Importance of Online Data: Understanding Preference Fine-tuning via Coverage. - Yuda Song 001; Gokul Swamy 001; Aarti Singh; J. Andrew Bagnell; Wen Sun 002 (NeurIPS, 2024)

REBEL: Reinforcement Learning via Regressing Relative Rewards. - Zhaolin Gao; Jonathan D. Chang; Wenhao Zhan; Owen Oertell; Gokul Swamy 001; Kianté Brantley; Thorsten Joachims; Drew Bagnell; Jason D. Lee; Wen Sun 002 (NeurIPS, 2024)

Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. - Jingwu Tang; Gokul Swamy 001; Fei Fang 001; Zhiwei Steven Wu (NeurIPS, 2024)

Understanding Preference Fine-Tuning Through the Lens of Coverage. - Yuda Song 001; Gokul Swamy 001; Aarti Singh; J. Andrew Bagnell; Wen Sun 002 (CoRR, 2024)

Efficient Imitation under Misspecification. - Nicolas A. Espinosa Dice; Sanjiban Choudhury; Wen Sun 002; Gokul Swamy 001 (ICLR, 2025)

Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF. - Zhaolin Gao; Wenhao Zhan; Jonathan Daniel Chang; Gokul Swamy 001; Kianté Brantley; Jason D. Lee; Wen Sun 002 (ICLR, 2025)

Diffusing States and Matching Scores: A New Framework for Imitation Learning. - Runzhe Wu; Yiding Chen; Gokul Swamy 001; Kianté Brantley; Wen Sun 002 (ICLR, 2025)

All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning. - Gokul Swamy 001; Sanjiban Choudhury; Wen Sun 002; Zhiwei Steven Wu; J. Andrew Bagnell (CoRR, 2025)

Scaling Offline RL via Efficient and Expressive Shortcut Models. - Nicolas A. Espinosa Dice; Yiyi Zhang; Yiding Chen; Bradley Guo; Owen Oertell; Gokul Swamy 001; Kianté Brantley; Wen Sun 002 (CoRR, 2025)

A Smooth Sea Never Made a Skilled SAILOR: Robust Imitation via Learning to Search. - Arnav Kumar Jain; Vibhakar Mohta; Subin Kim; Atiksh Bhardwaj; Juntao Ren; Yunhai Feng; Sanjiban Choudhury; Gokul Swamy 001 (CoRR, 2025)