As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

In this article, we review probabilistic topic models: graphical models that can be used to
summarize a large collection of documents with a smaller number of distributions over …

Tropospheric ozone is one of the six criteria pollutants regulated by the United States
Environmental Protection Agency under the Clean Air Act and has been linked with several …

For exchangeable data, mixture models are an extremely useful tool for density estimation
due to their attractive balance between smoothness and flexibility. When additional …

A logistic stick-breaking process (LSBP) is proposed for non-parametric clustering of general
spatially-or temporally-dependent data, imposing the belief that proximate data are more …

Facial beauty analysis is an important topic in human society. It may be used as a guidance
for face beautification applications such as cosmetic surgery. Deep neural networks (DNNs) …

Estimating the probability of extreme temperature events is difficult because of limited
records across time and the need to extrapolate the distributions of these events, as …

This paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning
(NC-CMTL), for learning multiple related regression tasks jointly. Instead of utilizing the …

Dependent nonparametric processes extend distributions over measures, such as the
Dirichlet process and the beta process, to give distributions over collections of measures …