Workshop on Graph Partitioning in Vision and Machine Learning

January 9-11, 2003
Carnegie Mellon University, Pittsburgh, Pennsylvania

Graph cuts and separators of various forms have a long history in Algorithms. More recently, they have been used in Computer Vision for problems of image segmentation and data cleaning, among others. In Machine Learning, there has been increasing interest in problems of learning from labeled and unlabeled data, as well as probabilistic inference when data have pairwise relationships, that seem closely related to notions of graph partitioning. However, in each area, the objectives are subtly different, and it's not always clear how to best formalize them. The purpose of this workshop is to bring together researchers in Algorithms, Vision, and Machine Learning around the subject of graph partitioning and other graph algorithms, in order to discuss and better understand the connections between these problems and the techniques used to solve them. The workshop will be a combination of survey talks, new results and informal discussion. There will be no published proceedings, but we plan to have a web page for the workshop with abstracts, slides, and pointers to relevant papers.
Registration is free. To register, contact Sophie.Park@cs.cmu.edu.

Organizing committee: Avrim Blum, John Lafferty, Jon Kleinberg, Jianbo Shi, Eva Tardos, Ramin Zabih

 

Schedule. (All events in Wean 5409 unless otherwise specified. )
See the PROBE

 

This material is based upon work supported by National Science Foundation under Grant No. 0122581.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the
National Science Foundation