Approximation, sampling, and compression in high dimensional problems
| Created: | 2019-06-18 08:28 |
|---|---|
| Institution: | Isaac Newton Institute for Mathematical Sciences |
| Description: | In a number of problems, both in theory and applications, one faces a situation when the ambient dimension is extremely high. Such problems often include approximating, sampling, or compressing functions on high-dimensional domains. Classical methods fail to be effective in this case due to the effect known as `curse of dimensionality'; hence new tools and algorithms need to be devised. Compressed sensing, which has gained great popularity in this century, is one example of a circle of ideas which make high-dimensional problems feasible. Methods which allow one to overcome the curse of dimensionality come from a mixture of mathematical fields: approximation, probability, functional and harmonic analysis, linear algebra, combinatorics, geometry, etc. In addition to pure mathematical interest, this field has great importance in numerous applications, in particular in data science and signal processing. Despite decades of research, many important questions in this area are still open. This workshop will bring together researchers in pure and applied mathematics, who attack high-dimensional problems. |
Media items
This collection contains 14 media items.
Media items
Beating the Curse of Dimensionality: A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
Kutyniok, G
Thursday 20th June 2019 - 14:20 to 15:10
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jun 2019
On some theorems on the restriction of operator to coordinate subspace
Kashin, B
20th June 2019 - 11:10 to 12:00
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jun 2019
A sequence of well-conditioned polynomials
Ortega-Cerdà, J
Tuesday 18th June 2019 - 13:30 to 14:20
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 19 Jun 2019
Basis properties of the Haar system in various function spaces
Seeger, A
Wednesday 19th June 2019 - 09:00 to 09:50
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 20 Jun 2019
Discrete translates in function spaces
Olevskii, A
Tuesday 18th June 2019 - 09:50 to 10:40
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 19 Jun 2019
Dynamical sampling and frames generated from powers of exponential operators
Aldroubi, A
Monday 17th June 2019 - 09:50 to 10:40
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 18 Jun 2019
High Dimensional Approximation via Sparse Occupancy Trees
Binev, P
Monday 17th June 2019 - 14:20 to 15:10
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 18 Jun 2019
Integral norm discretization and related problems
Dai, F
Friday 21st June 2019 - 11:10 to 12:00
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jun 2019
Linear and one-bit compressive sensing with subsampled random convolutions
Rauhut, H
Tuesday 18th June 2019 - 14:20 to 15:10
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 19 Jun 2019
Markov-type inequalities and extreme zeros of orthogonal polynomials
Nikolov, G
Friday 21st June 2019 - 14:20 to 15:10
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jun 2019
Quasi-Monte Carlo integration in uncertainty quantification of elliptic PDEs with log-Gaussian coefficients
Herrmann, L
Tuesday 18th June 2019 - 15:40 to 16:30
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 19 Jun 2019
Representer theorems and convex optimization
Boyer, C
Monday 17th June 2019 - 15:40 to 16:30
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 18 Jun 2019
Totally positive functions in sampling theory and time-frequency analysis
Groechenig, K
Friday 21st June 2019 - 09:50 to 10:40
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jun 2019
Transportation cost spaces on finite metric spaces
Kutzarova, D
Monday 17th June 2019 - 11:10 to 12:00
Collection: Approximation, sampling, and compression in high dimensional problems
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 18 Jun 2019