115 citations · 225 across the 6 of their papers we have counts for
9 papers
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson +1
We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability. We propose the harmonic k…
Nonparametric Score Estimators
Yuhao Zhou, Jiaxin Shi, Jun Zhu
Estimating the score, i.e., the gradient of log density function, from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of prob…
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Yang Song, Sahaj Garg, Jiaxin Shi +1
Score matching is a popular method for estimating unnormalized statistical models. However, it has been so far limited to simple, shallow models or low-dimensional data, due to the…
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an al…
Functional Variational Bayesian Neural Networks
Shengyang Sun, Guodong Zhang, Jiaxin Shi +1
Variational Bayesian neural networks (BNNs) perform variational inference over weights, but it is difficult to specify meaningful priors and approximate posteriors in a high-dimens…
Semi-crowdsourced Clustering with Deep Generative Models
Yucen Luo, Tian Tian, Jiaxin Shi +2
We consider the semi-supervised clustering problem where crowdsourcing provides noisy information about the pairwise comparisons on a small subset of data, i.e., whether a sample p…