31 citations · 162 across the 19 of their papers we have counts for
19 papers
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…
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
Kexin Chen, Jiamin Lu, Junyou Li +12
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society. This study proposes Chemist-X, a comprehensive AI agent that automates the…
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…
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Zongbo Han, Zhipeng Liang, Fan Yang +7
Subpopulation shift widely exists in many real-world machine learning applications, referring to the training and test distributions containing the same subpopulation groups but va…
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification
Liang Zeng, Lanqing Li, Ziqi Gao +2
Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, th…
Robust Imitation Learning from Corrupted Demonstrations
Liu Liu, Ziyang Tang, Lanqing Li +1
We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers. Classical approaches such as Behavio…
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
Yuanfeng Ji, Lu Zhang, Jiaxiang Wu +16
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its…
Value Penalized Q-Learning for Recommender Systems
Chengqian Gao, Ke Xu, Kuangqi Zhou +4
Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improvi…
Local Augmentation for Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong +6
Graph Neural Networks (GNNs) have achieved remarkable performance on graph-based tasks. The key idea for GNNs is to obtain informative representation through aggregating informatio…
IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control
Xiaoyan Cao, Yao Yao, Lanqing Li +8
Agriculture is the foundation of human civilization. However, the rapid increase of the global population poses a challenge on this cornerstone by demanding more food. Modern auton…
Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning
Rui Yang, Jiafei Lyu, Yu Yang +5
Multi-goal reinforcement learning is widely applied in planning and robot manipulation. Two main challenges in multi-goal reinforcement learning are sparse rewards and sample ineff…
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
Lanqing Li, Yuanhao Huang, Mingzhe Chen +3
Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications. A pop…
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
Lanqing Li, Rui Yang, Dijun Luo
We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks without any intera…
A Theory of Self-Resonance After Inflation, Part 2: Quantum Mechanics and Particle-Antiparticle Asymmetry
Mark P. Hertzberg, Johanna Karouby, William G. Spitzer +2
We further develop a theory of self-resonance after inflation in a large class of models involving multiple scalar fields. We concentrate on inflaton potentials that carry an inter…
A Theory of Self-Resonance After Inflation, Part 1: Adiabatic and Isocurvature Goldstone Modes
Mark P. Hertzberg, Johanna Karouby, William G. Spitzer +2
We develop a theory of self-resonance after inflation. We study a large class of models involving multiple scalar fields with an internal symmetry. For illustration, we often speci…