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.

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.

we propose that the predictability in human mobility is a state and not a static trait of individuals. First, we show that time (of the week) explains people’s whereabouts more than the sequences of locations they visit. Then, we show that not only does predictability depend on time but also the type of activity an individual is engaged in, thus establishing the importance of contexts in human mobility..

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.

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.

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.