[Statlist] FDS Virtual Seminar by Ming Yuan (ETH-ITS and Columbia University), 18.05.2020

[Maurer Letizia]

We are glad to announce the following talk in the ETH Foundations of Data Science Seminar 

"Information Based Complexity of High Dimensional Sparse Functions"   
by Ming Yuan, Institute for Theoretical Studies, ETH Zurich and Columbia University

Time: Monday, 18.05.2020 at 10.00 h
Place: zoom lecture, https://ethz.zoom.us/j/98021339623, Meeting ID: 980 2133 9623 (zoom room opens 9.55h)

Abstract: We investigate the optimal sample complexity of recovering a general high dimensional smooth and sparse function based on point queries. Our result provides a precise characterization of the potential loss, or lack thereof, in information when restricting to point queries as opposed to the more general linear queries, as well as the benefit of randomization and adaption. In addition, we also developed a general framework for function approximation to mitigate the curse of dimensionality that can also be easily adapted to incorporate further structure such as lower order interactions, leading to sample complexities better than those obtained earlier in the literature.

Organisers: A. Bandeira, H. Bölcskei, P. Bühlmann, J. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.-A. Loeliger, M. Maathuis, N. Meinshausen, G. Rätsch, C. Uhler, S. van de Geer, F. Yang

Seminar website: https://math.ethz.ch/sfs/news-and-events/data-science-seminar.html