(*) denotes equal contribution.
Preprints
Neural Eigenfunctions Are Structured Representation Learners
Zhijie Deng*, Jiaxin Shi*, Hao Zhang, Peng Cui, Cewu Lu, Jun Zhu.
Refereed Conference Publications
Simplified and Generalized Masked Diffusion for Discrete Data
Jiaxin Shi*, Kehang Han*, Zhe Wang, Arnaud Doucet, Michalis K. Titsias.
NeurIPS 2024. [pdf] [abs] [slides]
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
Jiaxin Shi, Lester Mackey.
Sequence Modeling with Multiresolution Convolutional Memory
Jiaxin Shi, Ke Alexander Wang, Emily B. Fox.
Gradient Estimation with Discrete Stein Operators
Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey.
NeurIPS 2022. [pdf] [abs] [code]
NeurIPS 2022 Outstanding Paper Award
NeuralEF: Deconstructing Kernels by Deep Neural Networks
Zhijie Deng, Jiaxin Shi, Jun Zhu.
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models
Michalis K. Titsias, Jiaxin Shi.
AISTATS 2022. [pdf] [abs] [code]
Sampling with Mirrored Stein Operators
Jiaxin Shi, Chang Liu, Lester Mackey.
ICLR 2022. [pdf] [abs] [code] [slides]
Spotlight Presentation (top 5.1%).
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger Grosse.
Nonparametric Score Estimators
Yuhao Zhou, Jiaxin Shi, Jun Zhu.
ICML 2020. [pdf] [abs] [code] [slides]
Sparse Orthogonal Variational Inference for Gaussian Processes
Jiaxin Shi, Michalis K. Titsias, Andriy Mnih.
AISTATS 2020. [pdf] [abs] [code] [slides]
Best Student Paper Runner-Up at AABI Symposium, 2019.
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Yang Song*, Sahaj Garg*, Jiaxin Shi, Stefano Ermon.
UAI 2019. [pdf] [abs] [code] [video] [blog]
Oral Presentation (top 8.7%).
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu.
ICML 2019. [pdf] [abs] [code] [slides]
Functional Variational Bayesian Neural Networks
Shengyang Sun*, Guodong Zhang*, Jiaxin Shi*, Roger Grosse.
ICLR 2019. [pdf] [abs] [code] [video]
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu, Bo Zhang.
NeurIPS 2018. [pdf] [abs] [code]
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi, Shengyang Sun, Jun Zhu.
ICML 2018. [pdf] [abs] [code] [slides]
Message Passing Stein Variational Gradient Descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang.
Kernel Implicit Variational Inference
Jiaxin Shi*, Shengyang Sun*, Jun Zhu.
Workshop Papers
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
Ke Alexander Wang*, Matthew E. Levine*, Jiaxin Shi, Emily B. Fox. [pdf]
NeurIPS Workshop: Learning from Time Series for Health, 2022.
Neural Networks as Inter-domain Inducing Points
Shengyang Sun*, Jiaxin Shi*, Roger Grosse. [pdf] [slides] [video]
Symposium on Advances in Approximate Bayesian Inference, 2020.
Spectral Estimators for Gradient Fields of Log-Densities
Yuhao Zhou, Jiaxin Shi, Jun Zhu.
ICML Workshop on Stein’s Method, 2019.
Visualization & Graphics
Analyzing the Training Processes of Deep Generative Models
Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, Shixia Liu.
IEEE Transactions on Visualization and Computer Graphics, 2018. [pdf]
Towards Better Analysis of Deep Convolutional Neural Networks
Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu.
IEEE Transactions on Visualization and Computer Graphics, 2017. [pdf]
Most cited paper of TVCG in 2017.
Plenopatch: Patch-based Plenoptic Image Manipulation
Fanglue Zhang, Jue Wang, Eli Shechtman, Ziye Zhou, Jiaxin Shi, Shimin Hu.
IEEE Transactions on Visualization and Computer Graphics, 2017. [paper]