[Statlist] ETH Young Data Science Researcher Seminar Zurich, Virtual Seminar by Anna Ma, University of California

[Maurer Letizia]

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

"Gaussian Spherical Tessellations and Learning Adaptively“  
by Anna Ma, University of California

Time: Thursday, 24 February 2021, 15.00 - 16.00
Place: Zoom at https://ethz.zoom.us/j/62895316484


if you like to join, we meet at ETH Zürich, HG G 19.2, to watch the zoom together 


Abstract: Signed measurements of the form $y_i = sign(\langle a_i, x \rangle)$ for $i \in [M]$ are ubiquitous in large-​scale machine learning problems where the overarching task is to recover the unknown, unit norm signal $x \in \mathbb{R}^d$. Oftentimes, measurements can be queried adaptively, for example based on a current approximation of $x$, leading to only a subset of the $M$ measurements being needed. Geometrically, these measurements emit a spherical hyperplane tessellation in $\mathbb{R}^{d}$ where one of the cells in the tessellation contains the unknown vector $x$. Motivated by this problem, in this talk we will present a geometric property related to spherical hyperplane tessellations in $\mathbb{R}^{d}$. Under the assumption that $a_i$ are Gaussian random vectors, we will show that with high probability there exists a subset of the hyperplanes whose cardinality is on the order of $d\log(d)\log(M)$ such that the radius of the cell containing $x$ induced by these hyperplanes is bounded above by, up to constants, $d\log(d)\log(M)/M$. The work presented is joint work with Rayan Saab and Eric Lybrand.
M. Azadkia, G. Chinot, J. Hörrmann, M. Löffler, A. Taeb, N. Zhivotovskiy


Please take note of the mask requirements for joining this lecture. Further details can be found here, https://ethz.ch/services/en/news-and-events/coronavirus.html

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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