454 citations · 620 across the 7 of their papers we have counts for
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Mitigating Mismatch within Reference-based Preference Optimization
Suqin Yuan, Xingrui Yu, Jiyang Zheng +4
Direct Preference Optimization (DPO) has become the de facto standard for offline preference alignment of large language models, but its reliance on a reference policy introduces a…
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration
Heyang Zhao, Xingrui Yu, David M. Bossens +2
Imitation learning is a central problem in reinforcement learning where the goal is to learn a policy that mimics the expert's behavior. In practice, it is often challenging to lea…
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration
Xingrui Yu, Zhenglin Wan, David Mark Bossens +3
Learning diverse and high-performance behaviors from a limited set of demonstrations is a grand challenge. Traditional imitation learning methods usually fail in this task because…
Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity
Zhenglin Wan, Xingrui Yu, David Mark Bossens +5
Imitation learning (IL) has shown promise in various applications (e.g. robot locomotion) but is often limited to learning a single expert policy, constraining behavior diversity a…
Intrinsic Reward Driven Imitation Learning via Generative Model
Xingrui Yu, Yueming Lyu, Ivor W. Tsang
Imitation learning in a high-dimensional environment is challenging. Most inverse reinforcement learning (IRL) methods fail to outperform the demonstrator in such a high-dimensiona…
How does Disagreement Help Generalization against Label Corruption?
Xingrui Yu, Bo Han, Jiangchao Yao +3
Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances bec…
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han, Quanming Yao, Xingrui Yu +5
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during train…