EVENT DETAILS
SPEAKER NAME: David Bindel AFFILIATION: Cornell UniversityTITLE: Stochastic linear algebra for scalable Gaussian processes
Abstract:
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 Joint work with Kun Dong, David Eriksson, and Andrew Wilson. Bio: David Bindel is an associate professor in the department of Computer Science at Cornell University, where he is also associated with the fields of pure and applied mathematics, computational science and engineering, and civil engineering. Prior to Cornell, he was a Courant Instructor in the mathematics department at NYU (2006-2009) and a PhD student at Berkeley where he received his degree in computer science. He studies scientific computing in general and numerical linear algebra in particular, applied to the analysis of musical instruments, cell phones, microscopic gyroscopes, power grids, social and computer networks, topic models, and beyond.
TIME Tuesday October 9, 2018 at 11:00 AM - 12:00 PM
LOCATION M228, Technological Institute map it
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CONTACT Agnes Kaminski a-kaminski@northwestern.edu
CALENDAR Department of Industrial Engineering and Management Sciences