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The xyz algorithm for fast interaction search in high-dimensional data

Presented by: 
Rajen Shah University of Cambridge
Date: 
Thursday 18th January 2018 - 16:00 to 16:45
Venue: 
INI Seminar Room 1
Abstract: 
When performing regression on a dataset with $p$ variables, it is often of interest to go beyond using main effects and include interactions as products between individual variables. However, if the number of variables $p$ is large, as is common in genomic datasets, the computational cost of searching through $O(p^2)$ interactions can be prohibitive. In this talk I will introduce a new randomised algorithm called xyz that is able to discover interactions with high probability and under mild conditions has a runtime that is subquadratic in $p$. The underlying idea is to transform interaction search into a much simpler close pairs of points problem. We will see how strong interactions can be discovered in almost linear time, whilst finding weaker interactions requires $O(p^u)$ operations for $1<u<2$ depending on their strength. An application of xyz to a genome-wide association study shows how more than $10^{11}$ interactions can be screened in minutes using a standard laptop. This is joint work with Gian Thanei and Nicolai Meinshausen (ETH Zurich).
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons