Workshop description
The core question of information-based complexity (IBC) is: How many pieces of information are required to solve a (numerical) problem up to a prescribed error tolerance? The problems considered are manifold, including function approximation and learning, numerical integration, optimization, or the solution of PDEs and SDEs. The available information might be given by exact or noisy function values or other types of samples. It is of particular interest how the complexity increases with the dimensionality of the problem (cf. curse of dimensionality versus tractability) and with the desired accuracy (cf. rate of convergence). In view of the recent accomplishments of machine learning, another hot topic in IBC is the question in which situations the power of passive sampling (like iid samples) is comparable to the power of active sampling. We welcome anyone with similar interests to join us for fruitful discussions.
Speakers
Semi-plenary speakers
- Sonnleitner, Mathias - University of Passau, Germany
- Vybiral, Jan - TU Prague, Czech Republic
Invited speakers
- Bochacik, Tomasz - AGH UST Cracow, Poland
- Dolbeault, Matthieu - ENS Paris, France
- Giles, Mike - University of Oxford, UK
- Gnewuch, Michael - University of Osnabrück, Germany
- Goda, Takashi - University of Tokyo, Japan
- Griebel, Michael - University of Bonn, Germany
- Heinrich, Stefan - University of Kaiserslautern, Germany
- Kaluza, Andrzej - AGH UST Cracow, Poland
- Kritzer, Peter - RICAM Linz, Austria
- Kühn, Thomas - University of Leipzig, Germany
- Lemieux, Christiane - University of Waterloo, Canada
- Nuyens, Dirk - KU Leuven, Belgium
- Plaskota, Leszek - University of Warsaw, Poland
- Rudolf, Daniel - University of Passau, Germany
- Rüßmann, Robin - University of Kaiserslautern, Germany
- Sickel, Winfried - University of Jena, Germany
- Siedlecki, Pawel - University of Warsaw, Poland
- Sloan, Ian - UNSW Sydney, Australia
- Ullrich, Mario - JKU Linz, Austria
- Zani, Marguerite - Orleans University, France
Preliminary program
This schedule is preliminary and could be updated.
Monday, June 19
| 14:00 ~ 14:30 | Kernel method for parametric PDE with doubled convergence rate Ian Sloan - UNSW (Sydney), Australia |
| 14:30 ~ 15:00 | On probabilistic stability for selected randomized schemes for ODEs Tomasz Bochacik - AGH University of Science and Technology, Poland |
| 15:00 ~ 15:30 | Estimation of time-dependent parameters in SDEs-based models using neural networks Andrzej Kałuża - AGH University of Science and Technology in Krakow, Poland |
| 15:30 ~ 16:00 | On quadratures with optimal weights for spaces with bounded mixed derivatives Michael Griebel - INS, University of Bonn, Germany |
| 16:30 ~ 17:00 | Weighted least-squares approximation in expected $L^2$ norm Matthieu Dolbeault - Sorbonne Université, France |
| 17:00 ~ 17:30 | On Least Squares Approximation Based on Random or Optimal Data Mario Ullrich - JKU Linz, Österreich |
| 17:30 ~ 18:30 | The power of random information: recent results Mathias Sonnleitner - University of Passau, Germany |
Tuesday, June 20
Wednesday, June 21
| 14:00 ~ 14:30 | A universal numerical integration by digital nets Takashi Goda - University of Tokyo, Japan |
| 14:30 ~ 15:00 | Randomized lattice rules Dirk Nuyens - KU Leuven, Belgium |
| 15:00 ~ 16:00 | Lower bounds for numerical integration and approximation Jan Vybiral - Czech Technical University, Czech Republic |
| 16:30 ~ 17:00 | Quasi-random sampling with black box or acceptance-rejection inputs Christiane Lemieux - University of Waterloo, Canada |
| 17:00 ~ 17:30 | Consistency of randomized integration methods Daniel Rudolf - Universität Passau, Germany |
| 17:30 ~ 18:00 | $L^2$-approximation and numerical integration on Gaussian Spaces Robin Rüßmann - RPTU in Kaiserslautern, Germany |
| 18:00 ~ 18:30 | Tractability for additive random fields Marguerite Zani - Université d'Orléans, France |
Posters
- Data Compression using Lattice Rules for Machine Learning
Kumar Harsha - Osnabrück University, Germany - Efficient recovery of non-periodic functions via samples
Nicolas Nagel - TU Chemnitz, Germany - Sampling recovery in the uniform norm
Kateryna Pozharska - University of Technology, Chemnitz (IM NAS Ukraine), Germany (Ukraine) - Integration and function approximation on $\mathbb{R}^d$ using equispaced points and lattice points
Yuya Suzuki - Aalto University, Finland