[Statlist] Reminder: ETH Young Data Science Researcher Seminar Zurich, Virtual Seminar by Alden Green, Carnegie Mellon University

[Maurer Letizia]

We are glad to announce the following talk in the virtual ETH Young Data Science Researcher Seminar Zurich

"Statistical Theory for Nonparametric Regression with Graph Laplacians “  
by Alden Green, Carnegie Mellon University 

Time: Thursday, 17 March 2022, 15.00 - 16.00
Place: Zoom at https://ethz.zoom.us/j/62895316484

Abstract: Graph-​based learning refers to a family of conceptually simple and scalable approaches, which can be applied across many tasks and domains. We study graph-​based learning in a relatively classical setting: nonparametric regression with point-​cloud data lying on a (possibly) low-​dimensional data manifold. In this setting, many graph-​based methods can be interpreted as discrete approximations of “continuous-​time methods”-​--meaning methods defined with respect to continuous-​time differential operators—that serve as some of the traditional workhorses for nonparametric regression. Motivated by this connection, we develop theoretical guarantees for a pair of graph-​based methods, Laplacian eigenmaps and Laplacian smoothing, which show that they achieve optimal rates of convergence over Sobolev smoothness classes. Indeed, perhaps surprisingly, these results imply that graph-​based methods actually have superior properties than are suggested by tying them to standard continuous-​time tools.

M. Azadkia, G. Chinot, J. Hörrmann, M. Löffler, A. Taeb, N. Zhivotovskiy


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

Young Data Science Researcher Seminar Zurich – Seminar for Statistics | ETH Zurich
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