no code implementations • 20 Feb 2024 • Kexin Chen, Hanqun Cao, Junyou Li, Yuyang Du, Menghao Guo, Xin Zeng, Lanqing Li, Jiezhong Qiu, Pheng Ann Heng, Guangyong Chen
The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry.
no code implementations • 4 Feb 2024 • Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Junqiao Zhao, Pheng-Ann Heng
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 while acquiring knowledge safely.
no code implementations • 16 Nov 2023 • Kexin Chen, Junyou Li, Kunyi Wang, Yuyang Du, Jiahui Yu, Jiamin Lu, Lanqing Li, Jiezhong Qiu, Jianzhang Pan, Yi Huang, Qun Fang, Pheng Ann Heng, Guangyong Chen
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society.
no code implementations • 3 May 2023 • Liang Zeng, Lanqing Li, Jian Li
This paper studies this problem and proposes to incorporate chemical domain knowledge, specifically related to chemical reactions, for learning effective molecular representations.
no code implementations • 9 Apr 2023 • Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, QinGhua Hu, Bingzhe Wu, Changqing Zhang, Jianhua Yao
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 subpopulation proportions.
no code implementations • 13 Mar 2023 • Ziniu Li, Ke Xu, Liu Liu, Lanqing Li, Deheng Ye, Peilin Zhao
To address this issue, we propose an alternative framework that involves a human supervising the RL models and providing additional feedback in the online deployment phase.
1 code implementation • CVPR 2023 • Zhipeng Zhou, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Wei Gong
It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better.
1 code implementation • CVPR 2023 • Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang
It has been recently found that models trained with mixup also perform well on uncertainty calibration.
no code implementations • 30 Nov 2022 • Ziqi Gao, Yifan Niu, Jiashun Cheng, Jianheng Tang, Tingyang Xu, Peilin Zhao, Lanqing Li, Fugee Tsung, Jia Li
In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.
1 code implementation • 19 Sep 2022 • Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
1 code implementation • 16 Sep 2022 • Lanqing Li, Liang Zeng, Ziqi Gao, Shen Yuan, Yatao Bian, Bingzhe Wu, Hengtong Zhang, Yang Yu, Chan Lu, Zhipeng Zhou, Hongteng Xu, Jia Li, Peilin Zhao, Pheng-Ann Heng
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD).
no code implementations • 23 May 2022 • Liang Zeng, Lanqing Li, Ziqi Gao, Peilin Zhao, Jian Li
Motivated by this observation, we propose a principled GCL framework on Imbalanced node classification (ImGCL), which automatically and adaptively balances the representations learned from GCL without labels.
no code implementations • 29 Jan 2022 • Liu Liu, Ziyang Tang, Lanqing Li, Dijun Luo
We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers.
1 code implementation • 24 Jan 2022 • Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, Yatao Bian
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.
no code implementations • 15 Oct 2021 • Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan, Peilin Zhao
To alleviate the action distribution shift problem in extracting RL policy from static trajectories, we propose Value Penalized Q-learning (VPQ), an uncertainty-based offline RL algorithm.
1 code implementation • 8 Sep 2021 • Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu
To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.
1 code implementation • 6 Jul 2021 • Xiaoyan Cao, Yao Yao, Lanqing Li, Wanpeng Zhang, Zhicheng An, Zhong Zhang, Li Xiao, Shihui Guo, Xiaoyu Cao, Meihong Wu, Dijun Luo
However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task.
no code implementations • 25 Feb 2021 • Rui Yang, Jiafei Lyu, Yu Yang, Jiangpeng Yan, Feng Luo, Dijun Luo, Lanqing Li, Xiu Li
Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency.
no code implementations • 22 Feb 2021 • Lanqing Li, Yuanhao Huang, Mingzhe Chen, Siteng Luo, Dijun Luo, Junzhou Huang
Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications.
1 code implementation • ICLR 2021 • Lanqing Li, Rui Yang, Dijun Luo
In this work, we enforce behavior regularization on learned policy as a general approach to offline RL, combined with a deterministic context encoder for efficient task inference.