12 citations · 14 across the 6 of their papers we have counts for
7 papers · 1 filter
Sparse Infinite Random Feature Latent Variable Modeling
Michael Minyi Zhang
We propose a non-linear, Bayesian non-parametric latent variable model where the latent space is assumed to be sparse and infinite dimensional a priori using an Indian buffet proce…
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…
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Avinava Dubey, Michael Minyi Zhang, Eric P. Xing +1
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the f…
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Li-Fang Cheng, Bianca Dumitrascu, Michael Zhang +4
Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric framework that has proven useful in jointly modeling the physiological states of patients in medical tim…
A New Class of Time Dependent Latent Factor Models with Applications
Sinead A. Williamson, Michael Minyi Zhang, Paul Damien
In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as tempe…
Communication Efficient Parallel Algorithms for Optimization on Manifolds
Bayan Saparbayeva, Michael Minyi Zhang, Lizhen Lin
The last decade has witnessed an explosion in the development of models, theory and computational algorithms for "big data" analysis. In particular, distributed computing has serve…