One-week winter school on low-rank models, in the Swiss Alps from 12 to 17 January 2020.
The topic of the school is low-rank models and their use in numerical optimization and approximation. Specific focus will be given to modern applications in data science. Students will attend lectures and hands-on tutorials by leading experts.
Speakers (in alphabetical order) and topic of lectures:
- Nicolas Boumal (Princeton University)
An introduction to optimization on smooth manifolds
- Lieven De Lathauwer (KU Leuven)
An introduction to tensor decompositions and applications
- Ivan Oseledets (Skoltech)
Low-rank tensor approximations and deep learning
- Reinhold Schneider (TU Berlin)
Low rank approximation for high dimensional PDEs: Hamilton Jacobi Bellmann and variational Monte Carlo
- Madeleine Udell (Cornell University)
Big data is low rank: models, theory, and algorithms