Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems
that range from computational chemistry to health analytics, with a particular interest due to …

The growing demand for accurate, efficient, and scalable solutions in computational
mechanics highlights the need for advanced operator learning algorithms that can efficiently …

A key task in AutoML is to model learning curves of machine learning models jointly as a
function of model hyper-parameters and training progression. While Gaussian processes …

Scaling hyperparameter optimisation to very large datasets remains an open problem in the
Gaussian process community. This paper focuses on iterative methods, which use linear …

The Hilbert-space Gaussian Process (HGP) approach offers a hyperparameter-independent
basis function approximation for speeding up Gaussian Process (GP) inference by …

Gaussian processes are a versatile probabilistic machine learning model whose
effectiveness often depends on good hyperparameters, which are typically learned by …

Discovering new molecules empowers humanity to solve problems in health, agriculture,
energy, and more. The key challenge of molecule discovery is that the space of all possible …