Approximating high dimensional functions

Organisers: Aretha Teckentrup (University of Edinburgh and The Alan Turing Institute); Hemant Tyagi (University of Edinburgh and The Alan Turing Institute)

Date: 18 – 19 December 2017

Time: To be confirmed

Venue: The Alan Turing Institute

Registration opening soon.


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.

 

Confirmed speakers:

Jan Vybiral (Charles University, Prague)

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)

Arthur Gretton / Dougal Sutherland (UCL, UK)

Albert Cohen (Université Pierre et Marie Curie, Paris)

Sebastian Mayer (Universität Bonn, Germany)