This study reveals that human travel patterns are highly predictable, driven by basic needs and social influences, and shaped by factors from personal preferences to global disruptions like pandemics. By examining variations in time, activity, and location—known as “predictability states”—the research identifies unique contextual and activity signatures, which could improve short- and long-term mobility predictions, even with limited or incomplete data.

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.

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.