A survey on multi-task learning
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 …
leverage useful information contained in multiple related tasks to help improve the …
Remarks on multi-output Gaussian process regression
Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …
community. This article investigates the state-of-the-art multi-output Gaussian processes …
Variational Fourier features for 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 …
approach to sparse approximation and the spectral representation of Gaussian processes …
Gaussian process prior variational autoencoders
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 …
complex data distributions in an unsupervised fashion. One important limitation of VAEs is …
Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO
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 …
with applications from personalized health to single-cell biology. Existing factor analysis …
Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise
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 …
However, most of research focuses on noise handling in binary classification problems and …
Multi-target regression via robust low-rank learning
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 …
simultaneously learning multiple relevant regression tasks and its wide applications in data …
Bayesian optimization with high-dimensional outputs
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 …
applied to a small number of independent objectives. However, in practice we often wish to …
A multiple-phenotype imputation method for genetic studies
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 …
studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the …
Efficient set tests for the genetic analysis of correlated traits
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 …
genetic variants and quantitative traits. We describe mtSet (http://github. com/PMBio/limix), a …