4 papers
Stochastic Approximate Gradient Descent via the Langevin Algorithm
Yixuan Qiu, Xiao Wang
We introduce a novel and efficient algorithm called the stochastic approximate gradient descent (SAGD), as an alternative to the stochastic gradient descent for cases where unbiase…
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
Yixuan Qiu, Jing Lei, Kathryn Roeder
Sparse principal component analysis (PCA) is an important technique for dimensionality reduction of high-dimensional data. However, most existing sparse PCA algorithms are based on…
Exact and efficient inference for Partial Bayes problems
Yixuan Qiu, Lingsong Zhang, Chuanhai Liu
Bayesian methods are useful for statistical inference. However, real-world problems can be challenging using Bayesian methods when the data analyst has only limited prior knowledge…
Finite-sample bounds for the multivariate Behrens-Fisher distribution with proportional covariances
Yixuan Qiu, Lingsong Zhang
The Behrens-Fisher problem is a well-known hypothesis testing problem in statistics concerning two-sample mean comparison. In this article, we confirm one conjecture in Eaton and O…