TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed. - Shian Du; Yihong Luo; Wei Chen; Jian Xu 021; Delu Zeng (CVPR, 2022)

Double Normalizing Flows: Flexible Bayesian Gaussian Process ODEs Learning. - Jian Xu 021; Shian Du; Junmei Yang; Xinghao Ding; John W. Paisley; Delu Zeng (CoRR, 2023)

Sparse Variational Student-t Processes. - Jian Xu 021; Delu Zeng (AAAI, 2024)

Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference. - Jian Xu 021; Delu Zeng; John W. Paisley (ICML, 2024)

Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling. - Jian Xu 021; Zhiqi Lin; Shigui Li; Min Chen; Junmei Yang; Delu Zeng; John W. Paisley (CoRR, 2024)

Information Geometry and Beta Link for Optimizing Sparse Variational Student-t Processes. - Jian Xu 021; Delu Zeng; John W. Paisley (CoRR, 2024)

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling. - Jian Xu 021; Shian Du; Junmei Yang; Qianli Ma 001; Delu Zeng (CoRR, 2024)

Bayesian Gaussian Process ODEs via Double Normalizing Flows. - Jian Xu 021; Shian Du; Junmei Yang; Xinghao Ding; Delu Zeng; John Paisley (AISTATS, 2025)

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling. - Jian Xu 021; Shian Du; Junmei Yang; Qianli Ma 001; Delu Zeng; John Paisley (UAI, 2025)

Fully Bayesian differential Gaussian processes through stochastic differential equations. - Jian Xu 021; Zhiqi Lin; Min Chen; Junmei Yang; Delu Zeng; John W. Paisley (Knowl. Based Syst., 2025)

Neural Operator Variational Inference Based on Regularized Stein Discrepancy for Deep Gaussian Processes. - Jian Xu 021; Shian Du; Junmei Yang; Qianli Ma 001; Delu Zeng (IEEE Trans. Neural Networks Learn. Syst., 2025)