Approximating high dimensional functions
Date: 18 – 19 December 2017
Day 1: 9:00 – 19:30
Day 2: 9:00 – 14:00
Venue: The Alan Turing Institute
View the agenda here.
View abstracts here.
Registration for this event is now closed.
Many problems in science and engineering involve an underlying unknown complex process that depends on a large number of parameters. The goal in many applications is to reconstruct, or learn, the unknown process given some direct or indirect observations. Mathematically, such a problem can be reformulated as one of approximating a high dimensional function from limited information available, such as a small number of samples. In general, this problem is known to suffer from the curse of dimensionality – the number of samples required to achieve a certain accuracy in the function reconstruction typically scales exponentially with the number of parameters. Modern approaches bypass this issue by making additional structural assumptions on the function such as: low intrinsic dimensionality, partial separability, sparse representations in a basis etc. This has led to a number of results over the past decade, leading to a rich theory, for many interesting models.
The workshop will focus on the mathematical foundations of this problem, featuring talks by eminent researchers in the fields of multivariate approximation theory, high-dimensional integration, non-parametric regression and related areas.
Jan Vybiral (Czech Technical University, Czech Republic)
Sergey Dolgov (University of Bath, UK)
Sandra Keiper (Technische Universität Berlin, Germany)
Pierre Alquier (ENSAE, Universite Paris-Saclay, France)
Richard Samworth (University of Cambridge, UK)
Albert Cohen (Université Pierre et Marie Curie, France)
Sebastian Mayer (Universität Bonn and Fraunhofer SCAI, Germany)
Sören Wolfers (KAUST, Saudi Arabia)