Model Reduction for Edge-Weighted Personalized PageRank
Speaker: David Bindel, Cornell University
Location: Warren Weaver Hall 1302
Date: December 2, 2016, 11:30 a.m.
Host: Dennis Shasha
I describe work on model reduction for fast computation of PageRank for graphs in which the edge weights depend on parameters. For an example learning-to-rank application, our approach is nearly five orders of magnitude faster than the standard approach. This speed improvement enables interactive computation of a class of ranking results that previously could only be computed offline. While our approach draws on ideas common in model reduction for large physical simulations, the cost and accuracy tradeoffs for the edge-weighted PageRank problem are different, as we will describe.
This is joint work with Wenlei Xie, Johannes Gehrke, and Al Demers.
David S. Bindel is an assistant professor of computer science at Cornell University. Before joining Cornell, he was a Courant Instructor of mathematics at NYU. He works broadly in scientific computing, with a particular focus on numerical linear algebra applied to a variety of problems from computer science and electrical engineering. He received B.S. degrees in mathematics and computer science from the University of Maryland and a Ph.D. in computer science from UC Berkeley. He is the recipient of the Householder Prize, the SIAG/LA prize, and a Sloan fellowship.
In-person attendance only available to those with active NYU ID cards. All individuals must show the Daily Screener green pass in order to gain entry to the building.