ARC Colloquium: Anup Rao - Yale University

Event Details
Contact

Dani Denton

denton at cc dot gatech dot edu

Summaries

Summary Sentence: Anup Rao presents a talk as part of the ARC Colloquium series.

Full Summary: No summary paragraph submitted.

(Refreshments will be served in Klaus 2222 at 2 pm)

Title:
Algorithms for Lipschitz Learning on Graphs

Abstract:
We develop fast algorithms for solving regression problems on graphs where one is given the value of a function at some vertices, and must find its smoothest possible extension to all vertices. The extension we compute is the absolutely minimal Lipschitz extension, and is the limit for large p of p-Laplacian regularization. We present an algorithm that computes a minimal Lipschitz extension in expected linear time, and an algorithm that computes an absolutely minimal Lipschitz extension in expected time O(mn). The latter algorithm has variants that seem to run much faster in practice. These extensions are particularly amenable to regularization: we can perform l_0 regularization on the given values in polynomial time and l_1 regularization on the graph edge weights in time O(m^(3/2)). Our algorithms naturally extend to directed graphs.  This is a joint work with Rasmus Kyng, Sushant Sachdeva and Daniel Spielman.

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Additional Information

In Campus Calendar
No
Groups

College of Computing, School of Computer Science, ARC

Invited Audience
Undergraduate students, Faculty/Staff, Public, Graduate students
Categories
Seminar/Lecture/Colloquium
Keywords
(ARC), Adaptive Data Analysis, Algorithm and Randomness Center, Computational Complexity, Computational Learning Theory, Georgia Tech
Status
  • Created By: Dani Denton
  • Workflow Status: Published
  • Created On: Mar 9, 2015 - 12:57pm
  • Last Updated: Apr 13, 2017 - 5:19pm