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.
Sparse Orthogonal Variational Inference for Gaussian Processes
Jiaxin Shi, Michalis K. Titsias, and Andriy Mnih.
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.
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, and Jun Zhu.
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]