Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

A key problem in statistical modeling is model selection, that is, how to choose a model at an
appropriate level of complexity. This problem appears in many settings, most prominently in …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian
framework for modeling axial data (ie, observations are axes of direction) that can be …

The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric model that can be
used to model mixed-membership data with a potentially infinite number of components. It …

We consider the problem of speaker diarization, the problem of segmenting an audio
recording of a meeting into temporal segments corresponding to individual speakers. The …

This paper reviews the use of outlier detection approaches in urban traffic analysis. We
divide existing solutions into two main categories: flow outlier detection and trajectory outlier …

In multicenter studies, subjects in different centers may have different outcome distributions.
This article is motivated by the problem of nonparametric modeling of these distributions …

Outlier detection is an extensive research area, which has been intensively studied in
several domains such as biological sciences, medical diagnosis, surveillance, and traffic …

Clustering is an important data mining task for exploration and visualization of different data
types like news stories, scientific publications, weblogs, etc. Due to the evolving nature of …