Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee

1 code implementation • NeurIPS 2020 • Jincheng Bai, Qifan Song, Guang Cheng

Sparse deep learning aims to address the challenge of huge storage consumption by deep neural networks, and to recover the sparse structure of target functions.

Uncertainty Quantification Variable Selection +1

Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors

no code implementations • 24 Oct 2020 • Jincheng Bai, Qifan Song, Guang Cheng

We propose a variational Bayesian (VB) procedure for high-dimensional linear model inferences with heavy tail shrinkage priors, such as student-t prior.

Computational Efficiency regression +3

Stein Neural Sampler

1 code implementation • 8 Oct 2018 • Tianyang Hu, Zixiang Chen, Hanxi Sun, Jincheng Bai, Mao Ye, Guang Cheng

We propose two novel samplers to generate high-quality samples from a given (un-normalized) probability density.