12 citations · 51 across the 18 of their papers we have counts for
18 papers
A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation
Forough Fazeliasl, Michael Minyi Zhang, Bei Jiang +1
Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions with intractable likelihoods, imp…
Online Student- Processes with an Overall-local Scale Structure for Modelling Non-stationary Data
Taole Sha, Michael Minyi Zhang
Time-dependent data often exhibit characteristics, such as non-stationarity and heavy-tailed errors, that would be inappropriate to model with the typical assumptions used in popul…
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy
Forough Fazeli-Asl, Michael Minyi Zhang
We propose a novel generative model within the Bayesian non-parametric learning (BNPL) framework to address some notable failure modes in generative adversarial networks (GANs) and…
Bayesian Non-linear Latent Variable Modeling via Random Fourier Features
Michael Minyi Zhang, Gregory W. Gundersen, Barbara E. Engelhardt
The Gaussian process latent variable model (GPLVM) is a popular probabilistic method used for nonlinear dimension reduction, matrix factorization, and state-space modeling. Inferen…
A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks
Forough Fazeli-Asl, Michael Minyi Zhang, Lizhen Lin
A classic inferential statistical problem is the goodness-of-fit (GOF) test. Such a test can be challenging when the hypothesized parametric model has an intractable likelihood and…
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…
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds
Lizhen Lin, Bayan Saparbayeva, Michael Minyi Zhang +1
We propose a general scheme for solving convex and non-convex optimization problems on manifolds. The central idea is that, by adding a multiple of the squared retraction distance…
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…
Probabilistic Time of Arrival Localization
Fernando Perez-Cruz, Pablo M. Olmos, Michael Minyi Zhang +1
In this paper, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that…
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…
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson +1
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations ar…
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…
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models
Michael Minyi Zhang, Sinead A. Williamson, Fernando Perez-Cruz
Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing feat…
Parallel Markov Chain Monte Carlo for the Indian Buffet Process
Michael M. Zhang, Avinava Dubey, Sinead A. Williamson
Indian Buffet Process based models are an elegant way for discovering underlying features within a data set, but inference in such models can be slow. Inferring underlying features…
Embarrassingly Parallel Inference for Gaussian Processes
Michael Minyi Zhang, Sinead A. Williamson
Training Gaussian process-based models typically involves an computational bottleneck due to inverting the covariance matrix. Popular methods for overcoming this matrix i…
Robust and Parallel Bayesian Model Selection
Michael Minyi Zhang, Henry Lam, Lizhen Lin
Effective and accurate model selection is an important problem in modern data analysis. One of the major challenges is the computational burden required to handle large data sets t…