3 citations · 9 across the 20 of their papers we have counts for
6 papers · 1 filter
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard
Jingwu Tang, Gokul Swamy, Fei Fang +1
We study a multi-agent imitation learning (MAIL) problem where we take the perspective of a learner attempting to coordinate a group of agents based on demonstrations of an expert…
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift
Jiayun Wu, Jiashuo Liu, Peng Cui +1
We establish a new model-agnostic optimization framework for out-of-distribution generalization via multicalibration, a criterion that ensures a predictor is calibrated across a fa…
Provable Pluralistic Alignment: Multi-Party RLHF under Offline Human Feedback
Huiying Zhong, Zhun Deng, Tianwei Gao +3
Pluralistic alignment requires learning from feedback that reflects persistent and potentially conflicting stakeholder preferences while ultimately selecting a single collective po…
The Virtues of Pessimism in Inverse Reinforcement Learning
David Wu, Gokul Swamy, J. Andrew Bagnell +2
Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations. However, it traditionally requires repeatedly solving a comp…
Hybrid Inverse Reinforcement Learning
Juntao Ren, Gokul Swamy, Zhiwei Steven Wu +2
The inverse reinforcement learning approach to imitation learning is a double-edged sword. On the one hand, it can enable learning from a smaller number of expert demonstrations wi…
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
Gokul Swamy, Christoph Dann, Rahul Kidambi +2
We present Self-Play Preference Optimization (SPO), an algorithm for reinforcement learning from human feedback. Our approach is minimalist in that it does not require training a r…