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PROBE/Workshop on graph partitioning in Vision and Machine Learning

PROBE/Workshop on graph partitioning in Vision and Machine Learning. Organizers:. Avrim Blum CMU Algs/ML Jon Kleinberg Cornell Algs John Lafferty CMU ML Jianbo Shi U.Penn Vision Eva Tardos Cornell Algs Ramin Zabih Cornell Vision.

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PROBE/Workshop on graph partitioning in Vision and Machine Learning

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  1. PROBE/Workshop on graph partitioning in Vision and Machine Learning Organizers: Avrim Blum CMU Algs/ML Jon Kleinberg Cornell Algs John Lafferty CMU ML Jianbo Shi U.Penn Vision Eva Tardos Cornell Algs Ramin Zabih Cornell Vision

  2. PROBE/Workshop on graph partitioning in Vision and Machine Learning Executive summary: • Approaches that view task as kind of graph partitioning problem coming up recently in Computer Vision and Machine Learning. • Our goal: bring communities together. Relate objectives, techniques, experiences. • Successful workshop Jan 9-11, 2003 (21+64 attendees). Ongoing research projects.

  3. Undergrad: Raja Reddy PROBE/Workshop on graph partitioning in Vision and Machine Learning Grad students:

  4. Background/history • Graph partitioning problems have long history in algs & optimization. • max flow / min cut • balanced separators, min ratio cuts,... • k-median, facility location,...

  5. Background/history Graph partitioning problems have long history in algs & optimization. Recent use in computer vision: • Stereo image reconstruction. • [Greig,Porteous,Seheult], [Boykov,Veksler,Zabih] ... • Image segmentation. • [Shi, Malik],...

  6. S T What’s going on? • Fix up initial match using idea that most neighboring pixels should be at same depth. • Minimize “energy function”: cost for flipping label + pairwise cost.

  7. S T What’s going on? • A min-cut solves this exactly for case of 2 labels. Can get approximate (or good local optimal) for multiple labels. [BVZ][KT] • Empirically wins big over previous methods.

  8. Also in Machine Learning • Important topic in recent years: can large unlabeled dataset help with learning? • Often, have reason to believe two examples probably have same label, even if unsure what that label is: • Could be just similarity, or additional features. • “examples” take role of “pixels”.

  9. Graph partitioning in ML • Define graph with edges between similar examples (perhaps weighted). • Solve for best labeling (e.g., minimize weight of bad edges). - + + - View as MRF problem or graph cut problem. [Blum, Chawla], [Zhu, Ghahramani], [Z,G,Lafferty]...

  10. Graph partitioning in ML But then other issues: • What is “similar” anyway • Other assumptions/beliefs not modeled by graph structure or min-cut objective. • features, other info. • ...

  11. The PROBE Given all this, it seemed high time to get these groups/communities together. • Workshop on Graph Partitioning in Vision and Machine Learning • Research collaborations

  12. Research projects • Balcan, Zhu: learning from visual data. • Zhu, Ghahramani, Lafferty: Gaussian random fields. • Rwebangira, Reddy: use standard learning algorithm to set priors. • Bansal, B, Chawla, Cohen, McCallum: correlation clustering (formulation of learning-based clustering problem).

  13. Correlation clustering Cohen and McCallum: learning for entity-name clustering Bansal-Blum-Chawla: formulate as graph problem. Apx algs McCallum and Wellner: NLP coreference Demaine-Immorlica, Charikar et al, Emanuel-Fiat: improved LP-based algs, generalize results.

  14. From: Andrew McCallum mccallum@cs.umass.edu Subject: graph partitioning Date: 26 Jun 2003 16:28:51 –0400 Hi Avrim, Nikhil and Shuchi, I realized the other day that I hadn't yet sent you the paper on using graph partitioning for NLP coreference analysis... this is the paper related the the conference call we had a while ago earlier this year. We successfully used a minor variant on your "minimizing disagreements" correlational clustering algorithm to train the parameters of an undirected graphical model that performs NLP coreference. We are still making further feature-representation improvements, but already we are strongly out-performing several alternative algorithms that use identical feature sets, and also (barely) beating best-in-the-world performance on noun coreference in the MUC-7 dataset from a group at Cornell. I am becoming increasingly interested in graph partitioning algorithms, and would love to talk further. In particular, I'm especially interested in * algorithms that will scale nicely to thousands of nodes; * randomized algorithms whose posterior distribution over partitionings corresponds to the posterior distribution of the corresponding Markov random field.[...] Have you been thinking further in this area? Let's find some time to talk! Best wishes, Andrew

  15. Future plans • Continued research interactions. • Locally, working together with some Darpa projects in Vision and AI. • Second workshop in year or so.

  16. Now, on to Jerry’s presentation

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