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CNA Seminar Sivaram Ambikasaran
Title: Fast algorithms for data analysis
Abstract: Data analysis and statistical modeling have gained popularity in a variety of fields including machine learning, astronomy, geosciences, etc. One of the more frequently used tools in statistical modeling is Gaussian process, which is attractive due to its flexibility, robustness, rich mathematical and statistical underpinnings. However, practical use in large-scale problems remain out of reach due to large memory requirements, scaling as O(N^2), and extensive computational time to perform matrix algebra, scaling as O(N^2) or O(N^3). In this talk, I will discuss my contributions to some of the new developments in handling large dense covariance matrices in the context of Gaussian processes. More specifically, I will focus on O(N) dense linear algebra algorithms for inversion, determinant computation, symmetric square-root factorization, etc.
Date: Thursday, October 2, 2014
Time: 1:30 pm
Location: Wean Hall 7218
Submitted by: David Kinderlehrer