| 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, SY Philip IEEE transactions on knowledge and data engineering 35 (8), 8052-8072, 2022 | 1231 | 2022 |
| Invertible image rescaling M Xiao, S Zheng, C Liu, Y Wang, D He, G Ke, J Bian, Z Lin, TY Liu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 280 | 2020 |
| 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 | 128* | 2022 |
| 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 | 119 | 2021 |
| 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 | 116* | 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 | 99 | 2018 |
| Predicting equilibrium distributions for molecular systems with deep learning S Zheng, J He, C Liu, Y Shi, Z Lu, W Feng, F Ju, J Wang, J Zhu, Y Min, ... Nature Machine Intelligence 6 (5), 558-567, 2024 | 85* | 2024 |
| 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 | 82* | 2021 |
| Benchmarking graphormer on large-scale molecular modeling datasets Y Shi, S Zheng, G Ke, Y Shen, J You, J He, S Luo, C Liu, D He, TY Liu arXiv preprint arXiv:2203.04810, 2022 | 75 | 2022 |
| Riemannian Stein Variational Gradient Descent for Bayesian Inference C Liu, J Zhu Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 69 | 2018 |
| 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 | 60 | 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 | 55 | 2021 |
| The impact of large language models on scientific discovery: a preliminary study using gpt-4 MR AI4Science, MA Quantum arXiv preprint arXiv:2311.07361, 2023 | 44 | 2023 |
| MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures H Yang, C Hu, Y Zhou, X Liu, Y Shi, J Li, G Li, Z Chen, S Chen, C Zeni, ... arXiv preprint arXiv:2405.04967, 2024 | 43 | 2024 |
| Stochastic Gradient Geodesic MCMC Methods C Liu, J Zhu, Y Song Advances in Neural Information Processing Systems, 3009-3017, 2016 | 37 | 2016 |
| Invertible Rescaling Network and Its Extensions M Xiao, S Zheng, C Liu, Z Lin, TY Liu International Journal of Computer Vision 131 (1), 134-159, 2023 | 30 | 2023 |
| 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 | 30 | 2021 |
| Modeling Lost Information in Lossy Image Compression Y Wang, M Xiao, C Liu, S Zheng, TY Liu arXiv preprint arXiv:2006.11999, 2020 | 28 | 2020 |
| Sampling with Mirrored Stein Operators J Shi, C Liu, L Mackey International Conference on Learning Representations, 2022 | 25 | 2022 |
| Understanding MCMC Dynamics as Flows on the Wasserstein Space C Liu, J Zhuo, J Zhu International Conference on Machine Learning, 4093--4103, 2019 | 25 | 2019 |
Tie-Yan LiuPresident, Zhongguancun Academy | IEEE Fellow | ACM Fellow | AAIA FellowVerified email at bjzgca.edu.cn
