Numerical Analysis and Approximation Theory meets Data Science
Videos from BIRS Workshop
Wolfgang Dahmen, University of South Carolina
Monday Apr 23, 2018 09:05 - 10:03
Data and Models
Simone Brugiapaglia, Simon Fraser University
Monday Apr 23, 2018 11:18 - 11:58
Sparse High-dimensional Approximation from Highly Noisy Data
Eldad Haber, The University of British Columbia
Monday Apr 23, 2018 13:31 - 14:32
Neural Networks Motivated by Partial Differential Equations
Yifei Lou, University of Texas at Dallas
Monday Apr 23, 2018 15:01 - 15:47
Nonconvex Approaches in Data Science
Hoang Tran, Oak Ridge National Laboratory
Monday Apr 23, 2018 15:47 - 16:33
Unified Null Space Conditions for Sparse Approximations via Nonconvex Minimizations
CP Poon, University of Cambridge
Tuesday Apr 24, 2018 10:29 - 11:09
On Dual Certificates for the Compressive Off-the-Grid Recovery Problem
Dejan Slepcev, Carnegie Mellon University
Tuesday Apr 24, 2018 11:14 - 11:59
Asymptotics of Objective Functionals in Semi-Supervised Learning
Michael Griebel, Universitaet Bonn
Tuesday Apr 24, 2018 13:33 - 14:39
Manifold Learning by Sparse Grid Methods
Dirk Nuyens, KU Leuven
Tuesday Apr 24, 2018 14:59 - 15:45
The Multivariate Decomposition Method
James Nichols, Sorbonne Universités
Tuesday Apr 24, 2018 15:48 - 16:38
Greedy Approximation Selection with Data Assimilation
Carola Schönlieb, University of Cambridge
Wednesday Apr 25, 2018 09:02 - 10:12
Learning Regularisers for Imaging Inverse Problems: From Quotient Minimisation to Adversarial Neural Networks
Jose Perea, Northeastern university
Thursday Apr 26, 2018 10:32 - 11:15
Topological Dimensionality Reduction
Giang Tran, University of Waterloo
Thursday Apr 26, 2018 11:18 - 12:03
Sparse Recovery Guarantees for Dependent Data
Houman Owhadi, Caltech
Thursday Apr 26, 2018 13:33 - 14:30
A Game Theoretic Approach to Numerical Approximation and Algorithm Design
Armenak Petrosyan, Oak Ridge National Laboratory
Thursday Apr 26, 2018 15:41 - 16:19
Joint Sparse Recovery Through Manifold Optimization
