I am a PhD student (since 2015) in the Department of Computer Science at Tsinghua University, advised by Jun Zhu. My research interests are in the area of probabilistic methods and approximate inference. I have worked on topics including variational inference, Gaussian processes, kernel/spectral methods, generative models, and Bayesian neural networks.

I’m recently visiting Vector Institute until the end of the year. This summer I was a research scientist intern at DeepMind, London. Previously I was a research intern at RIKEN-AIP, Tokyo. 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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Sparse Orthogonal Variational Inference for Gaussian Processes

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

Preprint, 2019. [pdf] [arxiv]

Best Student Paper Run-Up at the 2nd Symposium on Advances in Approximate Bayesian Inference (AABI 2019).

A Spectral Approach to Gradient Estimation for Implicit Distributions

Jiaxin Shi, Shengyang Sun, and Jun Zhu.

ICML, 2018. [pdf] [arxiv] [code]

Scalable Training of Inference Networks for Gaussian-Process Models

Jiaxin Shi, Mohammad Emtiyaz Khan, and Jun Zhu.

ICML, 2019. [pdf] [arxiv] [code]

Semi-crowdsourced Clustering with Deep Generative Models

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

NeurIPS, 2018. [pdf] [arxiv] [code]

Software

I’m currently leading the development of ZhuSuan, a deep probabilistic programming library based on Tensorflow.

ZhuSuan: A Library for Bayesian Deep Learning

Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, and Yuhao Zhou.

Arxiv, 2017.

[github] [docs] [arxiv]

Curriculum Vitae

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