(*) denotes equal contribution.

Preprints

Sparse Orthogonal Variational Inference for Gaussian Processes

Jiaxin Shi, Michalis K. Titsias, and Andriy Mnih.

Arxiv, 2019. [pdf] [arxiv]

Conference Papers

Sliced Score Matching: A Scalable Approach to Density and Score Estimation

Yang Song*, Sahaj Garg*, Jiaxin Shi, Stefano Ermon.

The 35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019. [pdf] [arxiv] [code]

Scalable Training of Inference Networks for Gaussian-Process Models

Jiaxin Shi, Mohammad Emtiyaz Khan, and Jun Zhu.

International Conference on Machine Learning (ICML), 2019. [pdf] [arxiv] [code]

Functional Variational Bayesian Neural Networks

Shengyang Sun*, Guodong Zhang*, Jiaxin Shi*, Roger Grosse.

International Conference on Learning Representations (ICLR), 2019. [pdf] [arxiv] [code]

Semi-crowdsourced Clustering with Deep Generative Models

Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu and Bo Zhang.

Neural Information Processing Systems (NeurIPS), 2018. [pdf] [arxiv] [code]

A Spectral Approach to Gradient Estimation for Implicit Distributions

Jiaxin Shi, Shengyang Sun, and Jun Zhu.

International Conference on Machine Learning (ICML), 2018. [pdf] [arxiv] [code]

Message Passing Stein Variational Gradient Descent

Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, and Bo Zhang.

International Conference on Machine Learning (ICML), 2018. [pdf] [arxiv]

Kernel Implicit Variational Inference

Jiaxin Shi*, Shengyang Sun*, and Jun Zhu.

International Conference on Learning Representations (ICLR), 2018. [pdf] [arxiv]

Workshop Abstracts

Spectral Estimators for Gradient Fields of Log-Densities

Yuhao Zhou, Jiaxin Shi, and Jun Zhu.

ICML Workshop on Stein’s Method, Long Beach, USA, 2019.

Functional Variational Bayesian Neural Networks

Shengyang Sun*, Guodong Zhang*, Jiaxin Shi*, Roger Grosse.

NeurIPS Bayesian Deep Learning Workshop, Montréal, Canada, 2018. [pdf]

Semi-crowdsourced Clustering with Deep Generative Models

Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu and Bo Zhang.

ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden, 2018. [pdf]

Implicit Variational Inference with Kernel Density Ratio Fitting

Jiaxin Shi, Shengyang Sun and Jun Zhu.

ICML Workshop on Implicit Models, Sydney, Australia, 2017. [pdf]

Visualization & Graphics

Analyzing the Training Processes of Deep Generative Models

Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, and Shixia Liu.

IEEE Transactions on Visualization and Computer Graphics, 2018. [paper]

Towards Better Analysis of Deep Convolutional Neural Networks

Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, and Shixia Liu.

IEEE Transactions on Visualization and Computer Graphics, 2017. Most cited paper of TVCG 2017. [paper]

Plenopatch: Patch-based Plenoptic Image Manipulation

Fanglue Zhang, Jue Wang, Eli Shechtman, Ziye Zhou, Jiaxin Shi, and Shimin Hu.

IEEE Transactions on Visualization and Computer Graphics, 2017. [paper]