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Chang Liu
Chang Liu
Microsoft Research Asia
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Generalizing to unseen domains: A survey on domain generalization
J Wang, C Lan, C Liu, Y Ouyang, T Qin, W Lu, Y Chen, W Zeng, P Yu
IEEE Transactions on Knowledge and Data Engineering, 2022
1532022
Invertible image rescaling
M Xiao, S Zheng, C Liu, Y Wang, D He, G Ke, J Bian, Z Lin, TY Liu
European Conference on Computer Vision, 126-144, 2020
772020
Understanding and Accelerating Particle-Based Variational Inference
C Liu, J Zhuo, P Cheng, R Zhang, J Zhu, L Carin
International Conference on Machine Learning, 4082--4092, 2019
58*2019
Riemannian Stein Variational Gradient Descent for Bayesian Inference
C Liu, J Zhu
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
522018
Message Passing Stein Variational Gradient Descent
J Zhuo, C Liu, J Shi, J Zhu, N Chen, B Zhang
International Conference on Machine Learning, 2018
522018
Stochastic Gradient Geodesic MCMC Methods
C Liu, J Zhu, Y Song
Advances in Neural Information Processing Systems, 3009-3017, 2016
282016
Learning Causal Semantic Representation for Out-of-Distribution Prediction
C Liu, X Sun, J Wang, H Tang, T Li, T Qin, W Chen, TY Liu
Advances in Neural Information Processing Systems 34, 2021
192021
Understanding MCMC Dynamics as Flows on the Wasserstein Space
C Liu, J Zhuo, J Zhu
International Conference on Machine Learning, 4093--4103, 2019
152019
Variational Annealing of GANs: A Langevin Perspective
C Tao, S Dai, L Chen, K Bai, J Chen, C Liu, R Zhang, G Bobashev, ...
International Conference on Machine Learning, 6176-6185, 2019
152019
Recovering Latent Causal Factor for Generalization to Distributional Shifts
X Sun, B Wu, X Zheng, C Liu, W Chen, T Qin, TY Liu
Advances in Neural Information Processing Systems 34, 2021
14*2021
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior
S Lee, H Kim, C Shin, X Tan, C Liu, Q Meng, T Qin, W Chen, S Yoon, ...
arXiv preprint arXiv:2106.06406, 2021
142021
Modeling Lost Information in Lossy Image Compression
Y Wang, M Xiao, C Liu, S Zheng, TY Liu
arXiv preprint arXiv:2006.11999, 2020
82020
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
MH Zhu, C Liu, J Zhu
International Conference on Machine Learning, 9500--9511, 2020
72020
Straight-Through Estimator as Projected Wasserstein Gradient Flow
P Cheng, C Liu, C Li, D Shen, R Henao, L Carin
NeurIPS 2018 Bayesian Deep Learning Workshop, 2018
72018
Sampling with Mirrored Stein Operators
J Shi, C Liu, L Mackey
arXiv preprint arXiv:2106.12506, 2021
52021
Direct Molecular Conformation Generation
J Zhu, Y Xia, C Liu, L Wu, S Xie, T Wang, Y Wang, W Zhou, T Qin, H Li, ...
arXiv preprint arXiv:2202.01356, 2022
42022
Mimicking atmospheric photochemical modeling with a deep neural network
J Xing, S Zheng, S Li, L Huang, X Wang, JT Kelly, S Wang, C Liu, C Jang, ...
Atmospheric Research 265, 105919, 2022
42022
Learning to Match Distributions for Domain Adaptation
C Yu, J Wang, C Liu, T Qin, R Xu, W Feng, Y Chen, TY Liu
arXiv preprint arXiv:2007.10791, 2020
32020
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
J Park, Y Seo, C Liu, L Zhao, T Qin, J Shin, TY Liu
Advances in Neural Information Processing Systems 34, 3029-3042, 2021
12021
Learning Invariant Representations across Domains and Tasks
J Wang, W Feng, C Liu, C Yu, M Du, R Xu, T Qin, TY Liu
arXiv preprint arXiv:2103.05114, 2021
12021
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Articles 1–20