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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:20241107T101157Z
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