Yao Xie

Papers on flow-based generative models

Flow-based generative models as iterative algorithms in probability space.
Yao Xie, Xiuyuan Cheng.
2025.

Flow-based conformal prediction for multi-dimensional time series.
Junghwan Lee, Chen Xu, Yao Xie.
2025.

Distributionally robust optimization via iterative algorithms in continuous probability spaces.
Linglingzhi Zhu, Yao Xie.
2024.

Local Flow Matching generative models.
Chen Xu, Xiuyuan Cheng, Yao Xie.
2024.

Annealing flow generative model towards sampling high-dimensional and multi-modal distributions.
Dongze Wu, Yao Xie.
2024.

Posterior sampling via Langevin dynamics based on generative priors.
Vishal Purohit, Matthew Repasky, Jianfeng Lu, Qiang Qiu, Yao Xie, Xiuyuan Cheng.
CVPR 2025.

Computing high-dimensional optimal transport by flow neural networks.
Chen Xu, Xiuyuan Cheng, Yao Xie.
AISTATS 2025.
Preliminary version presented at NeurIPS 2023 Workshop Optimal Transport and Machine Learning.

Flow-based distributionally robust optimization.
Chen Xu, Jonghyeok Lee, Xiuyuan Cheng, Yao Xie.
IEEE Journal on Selected Areas in Information Theory (JSAIT). 2024. Vol. 5. pp. 62-77.
Preliminary version presented in NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning.

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space.
Xiuyuan Cheng, Jianfeng Lu, Yixin Tan, and Yao Xie. (Authors listed alphabetically.)
IEEE Transactions on Information Theory. 2024. Vol. 70, No. 11. pp. 8087-8106.

Normalizing flow neural networks by JKO scheme. (GitHub).
Chen Xu, Xiuyuan Cheng, Yao Xie.
NeurIPS 2023 (Spotlight).

Invertible neural networks for graph prediction. (GitHub)
Chen Xu, Xiuyuan Cheng, Yao Xie.
IEEE Journal on Selected Areas in Information Theory (JSAIT). Vol. 3, No. 3, pp. 454-467. Sept. 2022.
Conference version presented at NeurIPS 2022 Workshop: New Frontiers in Graph Learning.