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 …

Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …

This work brings together two powerful concepts in Gaussian processes: the variational
approach to sparse approximation and the spectral representation of Gaussian processes …

Variational autoencoders (VAE) are a powerful and widely-used class of models to learn
complex data distributions in an unsupervised fashion. One important limitation of VAEs is …

Factor analysis is a widely used method for dimensionality reduction in genome biology,
with applications from personalized health to single-cell biology. Existing factor analysis …

The problem of class noisy instances is omnipresent in different classification problems.
However, most of research focuses on noise handling in binary classification problems and …

Multi-target regression has recently regained great popularity due to its capability of
simultaneously learning multiple relevant regression tasks and its wide applications in data …

Bayesian optimization is a sample-efficient black-box optimization procedure that is typically
applied to a small number of independent objectives. However, in practice we often wish to …

Genetic association studies have yielded a wealth of biological discoveries. However, these
studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the …

Set tests are a powerful approach for genome-wide association testing between groups of
genetic variants and quantitative traits. We describe mtSet (http://github. com/PMBio/limix), a …