Links between probabilistic and non-probabilistic learning algorithms can arise by
performing small-variance asymptotics, ie, letting the variance of particular distributions in a …

The discovery, extraction and analysis of knowledge from data rely generally upon the use
of unsupervised learning methods, in particular clustering approaches. Much recent …

This paper applies a robust statistical scheme to the problem of unsupervised learning of
high-dimensional data. We develop, analyze, and apply a new finite mixture model based …

We present a truncation-free online variational inference algorithm for Bayesian
nonparametric models. Unlike traditional (online) variational inference algorithms that …

Hidden Markov random field (HMRF) models are widely used for image segmentation, as
they appear naturally in problems where a spatially constrained clustering scheme is asked …

Bayesian Ying–Yang (BYY) learning has provided a new mechanism that makes parameter
learning with automated model selection via maximizing a harmony function on a backward …

Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture
models, have shown great promise for density estimation and data clustering. Given the size …

Many applications call for learning to label individual objects in an image where the only
information available to the learner is a dataset of images with their associated captions, ie …

We develop an automated variational method for approximate inference in Gaussian
process (GP) models whose posteriors are often intractable. Using a mixture of Gaussians …

In this paper, we present a fully Bayesian approach for generalized Dirichlet mixtures
estimation and selection. The estimation of the parameters is based on the Monte Carlo …