Generative models have emerged as a promising technique for producing
hi...
The Gaussian process latent variable model (GPLVM) is a popular probabil...
A classic inferential problem in statistics is the two-sample hypothesis...
We propose a non-linear, Bayesian non-parametric latent variable model w...
We propose a general scheme for solving convex and non-convex optimizati...
Gaussian process-based latent variable models are flexible and theoretic...
Bayesian nonparametric (BNP) models provide elegant methods for discover...
In this paper, we take a new approach for time of arrival geo-localizati...
Many machine learning problems can be framed in the context of estimatin...
In many applications, observed data are influenced by some combination o...
The last decade has witnessed an explosion in the development of models,...
Inference of latent feature models in the Bayesian nonparametric setting...
Effective and accurate model selection is an important problem in modern...