95 citations · 196 across the 14 of their papers we have counts for
7 papers · 1 filter
Learning Topic Models: Identifiability and Finite-Sample Analysis
Yinyin Chen, Shishuang He, Yun Yang +1
Topic models provide a useful text-mining tool for learning, extracting, and discovering latent structures in large text corpora. Although a plethora of methods have been proposed…
Bayesian Regularization for Graphical Models with Unequal Shrinkage
Lingrui Gan, Naveen N. Narisetty, Feng Liang
We consider a Bayesian framework for estimating a high-dimensional sparse precision matrix, in which adaptive shrinkage and sparsity are induced by a mixture of Laplace priors. Bes…
Discriminative Similarity for Clustering and Semi-Supervised Learning
Yingzhen Yang, Feng Liang, Nebojsa Jojic +3
Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise…
An Empirical Bayes Approach for High Dimensional Classification
Yunbo Ouyang, Feng Liang
We propose an empirical Bayes estimator based on Dirichlet process mixture model for estimating the sparse normalized mean difference, which could be directly applied to the high d…
Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm
Daniel Khashabi, John Wieting, Jeffrey Yufei Liu +1
In this paper, we propose a model-based clustering method (TVClust) that robustly incorporates noisy side information as soft-constraints and aims to seek a consensus between side…
PAC-Bayesian AUC classification and scoring
James Ridgway, Pierre Alquier, Nicolas Chopin +1
We develop a scoring and classification procedure based on the PAC-Bayesian approach and the AUC (Area Under Curve) criterion. We focus initially on the class of linear score funct…