1.2k citations · 7.6k across the 154 of their papers we have counts for
40 papers · 1 filter
FABind: Fast and Accurate Protein-Ligand Binding
Qizhi Pei, Kaiyuan Gao, Lijun Wu +7
Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements i…
De Novo Molecular Generation via Connection-aware Motif Mining
Zijie Geng, Shufang Xie, Yingce Xia +6
De novo molecular generation is an essential task for science discovery. Recently, fragment-based deep generative models have attracted much research attention due to their flexibi…
Regeneration Learning: A Learning Paradigm for Data Generation
Xu Tan, Tao Qin, Jiang Bian +2
Machine learning methods for conditional data generation usually build a mapping from source conditional data X to target data Y. The target Y (e.g., text, speech, music, image, vi…
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng +8
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluct…
Unified 2D and 3D Pre-Training of Molecular Representations
Jinhua Zhu, Yingce Xia, Lijun Wu +5
Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represent…
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
Jiawei Huang, Li Zhao, Tao Qin +3
We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on the…
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
Jiawei Huang, Jinglin Chen, Li Zhao +3
Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL). Despite the community's increasing interest, there lacks a formal t…
You May Not Need Ratio Clipping in PPO
Mingfei Sun, Vitaly Kurin, Guoqing Liu +4
Proximal Policy Optimization (PPO) methods learn a policy by iteratively performing multiple mini-batch optimization epochs of a surrogate objective with one set of sampled data. R…
Towards Generating Real-World Time Series Data
Hengzhi Pei, Kan Ren, Yuqing Yang +3
Time series data generation has drawn increasing attention in recent years. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usua…
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Jongjin Park, Younggyo Seo, Chang Liu +4
Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal…
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
Pushi Zhang, Xiaoyu Chen, Li Zhao +3
A growing trend for value-based reinforcement learning (RL) algorithms is to capture more information than scalar value functions in the value network. One of the most well-known m…
Supervised Off-Policy Ranking
Yue Jin, Yue Zhang, Tao Qin +4
Off-policy evaluation (OPE) is to evaluate a target policy with data generated by other policies. Most previous OPE methods focus on precisely estimating the true performance of a…
On the Generative Utility of Cyclic Conditionals
Chang Liu, Haoyue Tang, Tao Qin +2
We study whether and how can we model a joint distribution using two conditional models and that form a cycle. This is motivated by the observation that…
R-Drop: Regularized Dropout for Neural Networks
Xiaobo Liang, Lijun Wu, Juntao Li +6
Dropout is a powerful and widely used technique to regularize the training of deep neural networks. In this paper, we introduce a simple regularization strategy upon dropout in mod…
Learning Structures for Deep Neural Networks
Jinhui Yuan, Fei Pan, Chunting Zhou +2
In this paper, we focus on the unsupervised setting for structure learning of deep neural networks and propose to adopt the efficient coding principle, rooted in information theory…
Generalizing to Unseen Domains: A Survey on Domain Generalization
Jindong Wang, Cuiling Lan, Chang Liu +6
Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unsee…
Return-Based Contrastive Representation Learning for Reinforcement Learning
Guoqing Liu, Chuheng Zhang, Li Zhao +5
Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxi…
Latent Causal Invariant Model
Xinwei Sun, Botong Wu, Xiangyu Zheng +4
Current supervised learning can learn spurious correlation during the data-fitting process, imposing issues regarding interpretability, out-of-distribution (OOD) generalization, an…
Masked Contrastive Representation Learning for Reinforcement Learning
Jinhua Zhu, Yingce Xia, Lijun Wu +4
Improving sample efficiency is a key research problem in reinforcement learning (RL), and CURL, which uses contrastive learning to extract high-level features from raw pixels of in…
Learning to Match Distributions for Domain Adaptation
Chaohui Yu, Jindong Wang, Chang Liu +5
When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is diff…
Accuracy Prediction with Non-neural Model for Neural Architecture Search
Renqian Luo, Xu Tan, Rui Wang +3
Neural architecture search (NAS) with an accuracy predictor that predicts the accuracy of candidate architectures has drawn increasing attention due to its simplicity and effective…
Learning to Reweight with Deep Interactions
Yang Fan, Yingce Xia, Lijun Wu +5
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real t…
Dual Learning: Theoretical Study and an Algorithmic Extension
Zhibing Zhao, Yingce Xia, Tao Qin +2
Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc. The high-level idea of dual lea…
Semi-Supervised Neural Architecture Search
Renqian Luo, Xu Tan, Rui Wang +3
Neural architecture search (NAS) relies on a good controller to generate better architectures or predict the accuracy of given architectures. However, training the controller requi…