I am a PhD student (since 2015) in the Department of Computer Science at Tsinghua University, supervised by Jun Zhu. My research interests are in probabilistic machine learning and approximate Bayesian inference, including and not limited to these topics: variational inference, probabilistic kernel methods (e.g., Gaussian processes), spectral methods, generative models, and Bayesian deep learning.

I created and lead the development of ZhuSuan, an open-source probabilistic programming library for Bayesian deep learning. I was awarded the Microsoft Research Asia Fellowship for 2018. I received my B.E. from the Department of Computer Science and Technology at Tsinghua University.

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Research Highlights

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Scalable Training of Inference Networks for Gaussian-Process Models

Jiaxin Shi, Mohammad Emtiyaz Khan, and Jun Zhu.

International Conference on Machine Learning (ICML), 2019.

Functional Variational Bayesian Neural Networks

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

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

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]

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]

Software

I created and lead the development of ZhuSuan, a probabilistic programming library with a particular focus on Bayesian deep learning.

[github] [docs] [white paper]

Curriculum Vitae

My CV can be downloaded from this link: [pdf]