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Series: School of Mathematics Colloquium

In these lectures we discuss some statistical problems with an interesting combinatorial structure behind. We start by reviewing the "hidden clique" problem, a simple prototypical example with a surprisingly rich structure. We also discuss various "combinatorial" testing problems and their connections to high-dimensional random geometric graphs. Time permitting, we study the problem of estimating the mean of a random variable.

Series: Other Talks

Thanks are due to our colleague, Vladimir Koltchinskii, for arranging this visit. Please write to Vladimir if you would like to meet with Professor Gabor Lugosi during his visit, or for additional information.

Series: Analysis Seminar

TBA

Series: High Dimensional Seminar

TBA

Series: Research Horizons Seminar

Series: Geometry Topology Seminar

Series: Combinatorics Seminar

Series: ACO Student Seminar

We investigate whether the standard dimensionality reduction techniques
inadvertently produce data representations with different fidelity for
two different populations. We show on several real-world datasets, PCA
has higher reconstruction error on population
A than B (for example, women versus men or lower versus higher-educated
individuals). This can happen even when the dataset has similar number
of samples from A and B . This motivates our study of dimensionality
reduction techniques which maintain similar fidelity
for A as B . We give an efficient algorithm for finding a projection
which is nearly-optimal with respect to this measure, and evaluate it on
several datasets. This is a joint work with Uthaipon
Tantipongpipat, Jamie Morgenstern, Mohit Singh, and Santosh Vempala.

Series: Other Talks

Series: High Dimensional Seminar

TBA