4 citations · 10 across the 11 of their papers we have counts for
5 papers · 1 filter
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
Danqing Wang, Antonis Antoniades, Kha-Dinh Luong +6
Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many dom…
BPO: Staying Close to the Behavior LLM Creates Better Online LLM Alignment
Wenda Xu, Jiachen Li, William Yang Wang +1
Direct alignment from preferences (DAP) has emerged as a promising paradigm for aligning large language models (LLMs) to human desiderata from pre-collected, offline preference dat…
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Jiachen Li, Edwin Zhang, Ming Yin +3
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a poli…
Causal Balancing for Domain Generalization
Xinyi Wang, Michael Saxon, Jiachen Li +3
While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerabi…
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Jiachen Li, Shuo Cheng, Zhenyu Liao +3
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration. Following the principle of (OFU)…