Day 1: (January 9) 
Hour 
Speaker 
Title 
9:00 Bagels and coffee 
9:30 
Avrim Blum 
Introduction; graph partitioning for machine learning [ppt,
html,
pdf] 
10:00 
Ramin Zabih 
Recent Developments in GraphBased Energy
Minimization for Computer Vision 
10:30 Break +
DISCUSSION SESSION.
Chair: Avrim Blum 
11:30 
Moses Charikar 
Compact Representation Schemes from Rounding Algorithms
[pdf] 
12:00 
ShangHua Teng 
Spectral Methods, Graph Partitioning, and Clustering [pdf] 
12:30 Lunch 
2:00 
Tom Mitchell 
Learning about WebSiteSpecific Graph Structure [ppt, html] 
2:30 
Bob Murphy 
Location Proteomics: Determining an Optimal Partitioning of
Proteins based on Subcellular Location 
3:00 Break + DISCUSSION SESSION.
Chair: Jon Kleinberg 
4:00 
Jianbo Shi 
Finding (Un)usual Events in Video [ppt] [papers] 
4:30 
Dan Huttenlocher 
Pictorial Structures for Object Recognition 
5:00 
Olga Veksler 
Compact Windows for Visual Correspondence via Minimum Ratio
Cycle Algorithm [ppt, html] 
5:30 DISCUSSION
SESSION. Chair: Ramin Zabih 
Day 2: (January 10) 
Hour 
Speaker 
Title 
9:00 Bagels and coffee 
9:30 
Christos Faloutsos 
Data mining large graphs [ppt,
html, pdf] 
10:00 
Jon Kleinberg 
An Impossibility Theorem for Clustering 
10:30 Break + DISCUSSION
SESSION. Chair: Eva Tardos 
11:30 
David Karger 
Learning Markov Random Fields: Maximum
BoundedTreewidth Graphs 
12:00 
John Lafferty 
Random Walks, Random Fields, and Graph Kernels [ps,
pdf] 
12:30 Lunch 
2:00 
Yuri Boykov 
Cut Metrics and Geometry of Grid Graphs [ppt,
html] [paper
1] 
2:30 
Henry Schneiderman 
Object Recognition using Graphical Models to Exploit Sparse
Structuring of Statistical Dependency 
3:00 Break + DISCUSSION SESSION.
Chair: Jianbo Shi 
4:00 
Jerry Zhu 
SemiSupervised Learning with Label Propagation [pdf] 
4:30 
Thorsten Joachims 
Transductive Learning, LeaveOneOut, and Cuts 
5:00 
John Langford 
Nonlinear dimensionality reduction [ps,
pdf] 
5:30
DISCUSSION SESSION. Chair: John Lafferty 
Day 3: (January 11) 
Hour 
Speaker 
Title 
9:00 Bagels and coffee 
9:30 
Tom Dietterich 
Fitting Conditional Random Fields via Gradient Boosting
[ps,
pdf] 
10:00 
Short (15min) Talks 


Vladimir Kolmogorov 
What energy functions can be minimized by graph cuts? 

Pedro Felzenszwalb 
Efficient graphbased image segmentation. 

Zoubin Ghahramani 
Learning from Labeled and Unlabeled Data using Markov Random
Fields [pdf] 

Shuchi Chawla 
Learning using Graph Mincuts [ppt,
html] 

Stella Yu 
Cuts with Constraints [pdf]

11:45 GENERAL DISCUSSION, SUMMARY
AND CONCLUSIONS. 