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 process, kernel/spectral methods, generative models, and Bayesian deep learning.
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.
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, and Jun Zhu.
Functional Variational Bayesian Neural Networks
Shengyang Sun*, Guodong Zhang*, Jiaxin Shi*, Roger Grosse.
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi, Shengyang Sun, and Jun Zhu.
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Yang Song*, Sahaj Garg*, Jiaxin Shi, Stefano Ermon.
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu and Bo Zhang.
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.
My CV can be downloaded from this link: [pdf]