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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: David Bindel
DESCRIPTION:Title: Stochastic LA for scalable GPs\nSpeaker: David
	 Bindel\nAbstract: Gaussian processes (GPs) define a distribution over
	 functions that generalizes the multivariate normal distribution over
	 vector spaces. Long used as a tool for spatio-temporal statistical
	 modeling\, GPs are also a key part of the modern arsenal in machine
	 learning. Unfortunately\, Gaussian process regression and kernel
	 hyper-parameter estimation with $N$ training examples involve
	 manipulating a dense $N$-by-$N$ kernel matrix\, and standard
	 factorization-based approaches to the underlying linear algebra problems
	 have $O(N^3)$ scaling. For regression with a fixed covariance kernel\,
	 more scalable iterative methods based on fast matrix-vector
	 multiplication with the kernel matrices are available. However\, maximum
	 likelihood estimation of kernel hyper-parameters and computation of
	 conditional variances involve operations such as computing log
	 derivatives and their derivatives or extracting the diagonal part of a
	 Schur complement. New tools are needed to address these problems in a
	 scalable manner. In this talk\, we discuss our recent work on one such
	 set of tools\, based on a combination of Krylov subspace methods for
	 matrix solves and matrix function applications together with stochastic
	 estimators for the trace and diagonal of a matrix using only
	 matrix-vector multiplies.\n\nThis is joint work with Kun Dong\, David
	 Eriksson\, and Andrew Wilson.
LOCATION:Zoom
UID:2020-10-20
STATUS:TENTATIVE
DTSTART:20201020T172500Z
DTEND:20201020T184000Z
LAST-MODIFIED:20201019T161705Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T153528Z
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