Multivariate {G}aussian processes: definitions, examples and applications

In this paper, we propose a precise definition of multivariate Gaussian processes based on Gaussian measures on vector-valued function spaces, and provide an existence proof.

Contrasting social and non-social sources of predictability in human mobility

In this paper, we apply entropy and predictability measures to analyse and bound the predictive information of an individual's mobility pattern and the flow of that information from their top social ties and from their non-social colocators.

Tuning Fairness by Balancing Target Labels

In this paper, we focus on mitigating the harm incurred by a biased machine learning system that offers better outputs (e.g., loans, job interviews) for certain groups than for others. We show that bias in the output can naturally be controlled in probabilistic models by introducing a latent target output.

Multivariate Gaussian and Student-t process regression for multioutput prediction

In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student-t process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods.

How priors of initial hyperparameters affect Gaussian process regression models

In this paper, we provide the first empirical study on the problem of how prior affect Gaussian process regression using simulated and real data experiments.