6 papers
Non-negative matrix factorization algorithms generally improve topic model fits
Peter Carbonetto, Abhishek Sarkar, Zihao Wang +1
In an effort to develop topic modeling methods that can be quickly applied to large data sets, we revisit the problem of maximum-likelihood estimation in topic models. It is known,…
Empirical Bayes Shrinkage of Functional Effects, with Application to Analysis of Dynamic eQTLs
Ziang Zhang, Peter Carbonetto, Matthew Stephens
We introduce functional adaptive shrinkage (FASH), an empirical Bayes method for joint analysis of observation units in which each unit estimates an effect function at several valu…
A New Family of Poisson Non-negative Matrix Factorization Methods Using the Shifted Log Link
Eric Weine, Peter Carbonetto, Rafael A. Irizarry +1
Poisson non-negative matrix factorization (NMF) is a widely used method to find interpretable "parts-based" decompositions of count data. While many variants of Poisson NMF exist,…
Covariate-moderated Empirical Bayes Matrix Factorization
William R. P. Denault, Karl Tayeb, Peter Carbonetto +2
Matrix factorization is a fundamental method in statistics and machine learning for inferring and summarizing structure in multivariate data. Modern data sets often come with "side…
Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Yunqi Yang, Karl Tayeb, Peter Carbonetto +3
Motivated by genetic fine-mapping applications, we introduce a new approach to Bayesian variable selection regression (BVSR) for time-to-event (TTE) outcomes. This new approach is…
Gradient-based optimization for variational empirical Bayes multiple regression
Saikat Banerjee, Peter Carbonetto, Matthew Stephens
Variational empirical Bayes (VEB) methods provide a practically attractive approach to fitting large, sparse, multiple regression models. These methods usually use coordinate ascen…