21 citations · 33 across the 4 of their papers we have counts for
9 papers · 1 filter
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
Lanqing Li, Hai Zhang, Xinyu Zhang +4
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt…
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
Liang Zeng, Lanqing Li, Jian Li
How to effectively represent molecules is a long-standing challenge for molecular property prediction and drug discovery. This paper studies this problem and proposes to incorporat…
Reweighted Mixup for Subpopulation Shift
Zongbo Han, Zhipeng Liang, Fan Yang +8
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different…
Deploying Offline Reinforcement Learning with Human Feedback
Ziniu Li, Ke Xu, Liu Liu +3
Reinforcement learning (RL) has shown promise for decision-making tasks in real-world applications. One practical framework involves training parameterized policy models from an of…
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Ziqi Gao, Yifan Niu, Jiashun Cheng +6
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation…
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
Lanqing Li, Liang Zeng, Ziqi Gao +11
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD). However, real-world pharmaceutical datasets…