Approximation, sampling and compression in data science

Created: 2019-02-12 11:00
Institution: Isaac Newton Institute for Mathematical Sciences
Description: Programme Theme


Approximation theory is the study of simulating potentially extremely complicated functions, called target functions, with simpler, more easily computable functions called approximants. The purpose of the simulation could be to approximate values of the target function with respect to a given norm, to estimate the integral of the target function, or to compute its minimum value. Approximation theory's relationship with computer science and engineering encourages solutions that are efficient with regards to computation time and space. In addition, approximation theory problems may also deal with real-life restrictions on data, which can be incomplete, expensive, or noisy. As a result, approximation theory often overlaps with sampling and compression problems.

The main aim of this programme is to understand and solve challenging problems in the high-dimensional context, but this aim is dual. On one hand, we would like to use the high-dimensional context to understand classical approximation problems. For example, recent developments have revealed promising new directions towards a break-through in a set of classical unsolved problems related to sampling in hyperbolic cross approximations. On the other hand, we want to understand why classical multivariate approximation methods fail in the modern high-dimensional context and to find methods that will be better and more efficient for modern approximation in very high dimensions. This direction will focus on two conceptual steps: First, replacement of classical smoothness assumptions by structural assumptions, such as those of sparsity used by compressed sensing. Second, the use of a nonlinear method, for instance a greedy algorithm, to find an appropriate sparse approximant.

In order to achieve the goal the programme will bring together researchers from different fields to work in groups on modern problems of high-dimensional approximation and related topics. It will foster exchange between different groups of researchers and practitioners.
 

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Approximation of Ridge Functions and Sparse Additive Models

   8 views

Vybiral, J
Monday 18th February 2019 - 13:40 to 14:15

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Tue 19 Feb 2019


Ball Average Characterizations of Function Spaces

   5 views

Yang, D
Tuesday 19th February 2019 - 09:40 to 10:15

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Wed 20 Feb 2019


Discrete Spherical Averages

   9 views

Lacey, M
Monday 25th February 2019 - 15:00 to 16:00

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Mon 25 Feb 2019


Embedding and continuity envelopes of Besov-type spaces

   4 views

Yuan, W
Tuesday 19th February 2019 - 11:00 to 11:35

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Wed 20 Feb 2019


Exponential tractability of weighted tensor product problems

   3 views

Wozniakowski, H
Monday 18th February 2019 - 09:40 to 10:15

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Tue 19 Feb 2019


Least squares regression on sparse grids

   8 views

Bohn, B
Friday 22nd February 2019 - 09:40 to 10:15

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Fri 22 Feb 2019


Lecture 2: Complexity results for integration.

   5 views

Novak, E
Wednesday 13th February 2019 - 15:00 to 16:30

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Fri 15 Feb 2019


Metric Approximation of Set-Valued Functions

   13 views

Berdysheva, E
Tuesday 12th March 2019 - 15:00 to 16:30

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Wed 13 Mar 2019


Morrey sequence spaces

   5 views

Haroske, D
Monday 25th March 2019 - 11:00 to 12:00

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Wed 27 Mar 2019


On some lower bounds for Kolmogorov widths

   3 views

Malykhin, Y
Monday 18th February 2019 - 11:40 to 12:15

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Tue 19 Feb 2019


Optimal recovery using wavelet trees

   3 views

Weimar, M
Thursday 21st February 2019 - 14:20 to 14:55

Collection: Approximation, sampling and compression in data science

Institution: Isaac Newton Institute for Mathematical Sciences

Created: Thu 21 Feb 2019


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