Graph signal processing focuses on extending the theory and methodologies of standard signal processing to signals defined on the vertices of a graph. Increasingly popular because of the flexibility of the underlying structure, this research area can be applied in many contexts (such as (...)

We study the learning properties of nonparametric minimum norm interpolating estimators. In particular, we consider estimators defined by so called Matern kernels, and focus on the role of the kernels scale and smoothness. While common ML wisdom suggests estimators defined by large function (...)

We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises a set of particles to approximate a target probability distribution π∝exp(−V) on ℝ^d. In the population limit, SVGD performs gradient descent in the space of probability distributions on the KL divergence with respect (...)

Stochastic gradient descent (SGD) provides a simple and efficient way to solve a broad range of machine learning problems. Here, we focus on distribution regression (DR), involving two stages of sampling : Firstly, we regress from probability measures to real-valued responses. Secondly, we (...)

Sequential or temporal data arise in many fields of research, such as quantitative finance, medicine or computer vision. We will be concerned with a novel approach for sequential learning, called the signature method, and rooted in rough path theory. Its basic principle is to represent (...)