48 citations · 108 across the 26 of their papers we have counts for
5 papers · 1 filter
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…
Adaptive Variational Bayesian Inference for Sparse Deep Neural Network
Jincheng Bai, Qifan Song, Guang Cheng
In this work, we focus on variational Bayesian inference on the sparse Deep Neural Network (DNN) modeled under a class of spike-and-slab priors. Given a pre-specified sparse DNN st…
Optimal False Discovery Control of Minimax Estimator
Qifan Song, Guang Cheng
Two major research tasks lie at the heart of high dimensional data analysis: accurate parameter estimation and correct support recovery. The existing literature mostly aims for eit…
Nearly optimal Bayesian Shrinkage for High Dimensional Regression
Qifan Song, Faming Liang
During the past decade, shrinkage priors have received much attention in Bayesian analysis of high-dimensional data. This paper establishes the posterior consistency for high-dimen…
Weak Convergence Rates of Population versus Single-Chain Stochastic Approximation MCMC Algorithms
Qifan Song, Mingqi Wu, Faming Liang
In this paper, we establish the theory of weak convergence (toward a normal distribution) for both single-chain and population stochastic approximation MCMC algorithms. Based on th…