9 citations · 18 across the 8 of their papers we have counts for
3 papers · 1 filter
Probabilistic Contrastive Principal Component Analysis
Didong Li, Andrew Jones, Barbara Engelhardt
Dimension reduction is useful for exploratory data analysis. In many applications, it is of interest to discover variation that is enriched in a "foreground" dataset relative to a…
Latent variable modeling with random features
Gregory W. Gundersen, Michael Minyi Zhang, Barbara E. Engelhardt
Gaussian process-based latent variable models are flexible and theoretically grounded tools for nonlinear dimension reduction, but generalizing to non-Gaussian data likelihoods wit…
Nonparametric Deconvolution Models
Allison J. B. Chaney, Archit Verma, Young-suk Lee +1
We describe nonparametric deconvolution models (NDMs), a family of Bayesian nonparametric models for collections of data in which each observation is the average over the features…