48 citations · 108 across the 26 of their papers we have counts for
7 papers · 1 filter
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
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. Although tremendous…
Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors
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. Theoretically, we establish…
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts
Sehwan Kim, Qifan Song, Faming Liang
Bayesian deep learning offers a principled way to address many issues concerning safety of artificial intelligence (AI), such as model uncertainty,model interpretability, and predi…
On the Generalization Properties of Adversarial Training
Yue Xing, Qifan Song, Guang Cheng
Modern machine learning and deep learning models are shown to be vulnerable when testing data are slightly perturbed. Existing theoretical studies of adversarial training algorithm…
Bayesian Shrinkage towards Sharp Minimaxity
Qifan Song
Shrinkage prior are becoming more and more popular in Bayesian modeling for high dimensional sparse problems due to its computational efficiency. Recent works show that a polynomia…
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation
Yue Xing, Qifan Song, Guang Cheng
We consider a data corruption scenario in the classical Nearest Neighbors (-NN) algorithm, that is, the testing data are randomly perturbed. Under such a scenario, the impac…
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection
Qifan Song, Yan Sun, Mao Ye +1
Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small cla…