Over the last years, supervised learning (SL) has established itself as the state-of-the-art for
data-driven turbulence modeling. In the SL paradigm, models are trained based on a …

The acquisition of labels for supervised learning can be expensive. To improve the sample
efficiency of neural network regression, we study active learning methods that adaptively …

Gaussian processes are ubiquitous in machine learning, statistics, and applied
mathematics. They provide a exible modelling framework for approximating functions, whilst …

For supervised classification tasks that involve a large number of instances, we propose and
study a new efficient tool, namely the Local-to-Global Support Vector Machine (LGSVM) …

Data-dependent greedy algorithms in kernel spaces are known to provide fast converging
interpolants, while being extremely easy to implement and efficient to run. Despite this …

Thanks to their easy implementation via radial basis functions (RBFs), meshfree kernel
methods have proved to be an effective tool for, eg, scattered data interpolation, PDE …

This work investigates the prediction performance of the kriging predictors. We derive some
error bounds for the prediction error in terms of non-asymptotic probability under the uniform …

We consider Bayesian optimization using Gaussian Process models, also referred to as
kernel-based bandit optimization. We study the methodology of exploring the domain using …

For dynamical systems with a non hyperbolic equilibrium, it is possible to significantly
simplify the study of stability by means of the center manifold theory. This theory allows to …

The purpose of this study is to introduce a new approach to feature ranking for classification
tasks, called in what follows greedy feature selection. In statistical learning, feature selection …