| 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 | 372 | 2022 |
| 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 | 139 | 2020 |
| 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 | 81* | 2019 |
| Message Passing Stein Variational Gradient Descent J Zhuo, C Liu, J Shi, J Zhu, N Chen, B Zhang International Conference on Machine Learning, 2018 | 71 | 2018 |
| Riemannian Stein Variational Gradient Descent for Bayesian Inference C Liu, J Zhu Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 66 | 2018 |
| 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 | 44 | 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, ... International Conference on Learning Representations, 2022 | 37 | 2022 |
| 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 | 33* | 2021 |
| Stochastic Gradient Geodesic MCMC Methods C Liu, J Zhu, Y Song Advances in Neural Information Processing Systems, 3009-3017, 2016 | 32 | 2016 |
| Understanding MCMC Dynamics as Flows on the Wasserstein Space C Liu, J Zhuo, J Zhu International Conference on Machine Learning, 4093--4103, 2019 | 21 | 2019 |
| 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 | 18 | 2019 |
| Direct Molecular Conformation Generation J Zhu, Y Xia, C Liu, L Wu, S Xie, T Wang, Y Wang, W Zhou, T Qin, H Li, ... Transactions on Machine Learning Research, 2022 | 16 | 2022 |
| Modeling Lost Information in Lossy Image Compression Y Wang, M Xiao, C Liu, S Zheng, TY Liu arXiv preprint arXiv:2006.11999, 2020 | 15 | 2020 |
| Variance Reduction and Quasi-Newton for Particle-Based Variational Inference MH Zhu, C Liu, J Zhu International Conference on Machine Learning, 9500--9511, 2020 | 12 | 2020 |
| Sampling with Mirrored Stein Operators J Shi, C Liu, L Mackey International Conference on Learning Representations, 2022 | 11 | 2022 |
| Towards Generating Real-World Time Series Data H Pei, K Ren, Y Yang, C Liu, T Qin, D Li 2021 IEEE International Conference on Data Mining (ICDM), 469-478, 2021 | 10 | 2021 |
| 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 | 9 | 2022 |
| 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 | 9 | 2018 |
| 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 | 8 | 2021 |
| 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 | 8 | 2020 |
Tao QinSenior Principal Research Manager, Microsoft ResearchVerified email at microsoft.com
