Chang Liu

According to our database, Chang Liu authored at least 32 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning.

He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao

Nat. Comput. Sci., 2024

2023

Generalizing to Unseen Domains: A Survey on Domain Generalization.

Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, Philip S. Yu

IEEE Trans. Knowl. Data Eng., August, 2023

Invertible Rescaling Network and Its Extensions.

Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu

Int. J. Comput. Vis., 2023

M-OFDFT: Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning.

He Zhang, Siyuan Liu, Jiacheng You, Chang Liu, Shuxin Zheng, Ziheng Lu, Tong Wang, Nanning Zheng, Bin Shao

CoRR, 2023

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning.

Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu

CoRR, 2023

2022

Direct Molecular Conformation Generation.

Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Yusong Wang, Tong Wang, Tao Qin, Wengang Zhou, Houqiang Li, Haiguang Liu, Tie-Yan Liu

Trans. Mach. Learn. Res., 2022

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets.

Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, Tie-Yan Liu

CoRR, 2022

Direct Molecular Conformation Generation.

Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Tong Wang, Yusong Wang, Wengang Zhou, Tao Qin, Houqiang Li, Tie-Yan Liu

CoRR, 2022

Sampling with Mirrored Stein Operators.

Jiaxin Shi, Chang Liu, Lester Mackey

Proceedings of the Tenth International Conference on Learning Representations, 2022

PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior.

Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior.

Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu

CoRR, 2021

Learning Invariant Representations across Domains and Tasks.

Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, Tie-Yan Liu

CoRR, 2021

Recovering Latent Causal Factor for Generalization to Distributional Shifts.

Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning.

Jongjin Park, Younggyo Seo, Chang Liu, Li Zhao, Tao Qin, Jinwoo Shin, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Generative Utility of Cyclic Conditionals.

Chang Liu, Haoyue Tang, Tao Qin, Jintao Wang, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Causal Semantic Representation for Out-of-Distribution Prediction.

Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generalizing to Unseen Domains: A Survey on Domain Generalization.

Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Towards Generating Real-World Time Series Data.

Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li

Proceedings of the IEEE International Conference on Data Mining, 2021

2020

Latent Causal Invariant Model.

Xinwei Sun, Botong Wu, Chang Liu, Xiangyu Zheng, Wei Chen, Tao Qin, Tie-Yan Liu

CoRR, 2020

Learning Causal Semantic Representation for Out-of-Distribution Prediction.

Chang Liu, Xinwei Sun, Jindong Wang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu

CoRR, 2020

Learning to Match Distributions for Domain Adaptation.

Chaohui Yu, Jindong Wang, Chang Liu, Tao Qin, Renjun Xu, Wenjie Feng, Yiqiang Chen, Tie-Yan Liu

CoRR, 2020

Modeling Lost Information in Lossy Image Compression.

Yaolong Wang, Mingqing Xiao, Chang Liu, Shuxin Zheng, Tie-Yan Liu

CoRR, 2020

Variance Reduction and Quasi-Newton for Particle-Based Variational Inference.

Michael Zhu, Chang Liu, Jun Zhu

Proceedings of the 37th International Conference on Machine Learning, 2020

Invertible Image Rescaling.

Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Straight-Through Estimator as Projected Wasserstein Gradient Flow.

Pengyu Cheng, Chang Liu, Chunyuan Li, Dinghan Shen, Ricardo Henao, Lawrence Carin

CoRR, 2019

Variational Annealing of GANs: A Langevin Perspective.

Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy V. Bobashev, Lawrence Carin

Proceedings of the 36th International Conference on Machine Learning, 2019

Understanding and Accelerating Particle-Based Variational Inference.

Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu

Proceedings of the 36th International Conference on Machine Learning, 2019

Understanding MCMC Dynamics as Flows on the Wasserstein Space.

Chang Liu, Jingwei Zhuo, Jun Zhu

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Accelerated First-order Methods on the Wasserstein Space for Bayesian Inference.

Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, Lawrence Carin

CoRR, 2018

Message Passing Stein Variational Gradient Descent.

Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang

Proceedings of the 35th International Conference on Machine Learning, 2018

Riemannian Stein Variational Gradient Descent for Bayesian Inference.

Chang Liu, Jun Zhu

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2016

Stochastic Gradient Geodesic MCMC Methods.

Chang Liu, Jun Zhu, Yang Song

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016