A polynomial algorithm for the k-cut problem for fixed k
Mathematics of Operations Research
The Complexity of Multiterminal Cuts
SIAM Journal on Computing
Approximating s-t minimum cuts in Õ(n2) time
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Minimum cuts in near-linear time
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Random Sampling in Cut, Flow, and Network Design Problems
Mathematics of Operations Research
An improved approximation algorithm for MULTIWAY CUT
Journal of Computer and System Sciences - 30th annual ACM symposium on theory of computing
Introduction to Algorithms
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Network failure detection and graph connectivity
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
On the Number of Minimum Cuts in a Graph
SIAM Journal on Discrete Mathematics
Rounding Algorithms for a Geometric Embedding of Minimum Multiway Cut
Mathematics of Operations Research
Optimal 3-terminal cuts and linear programming
Mathematical Programming: Series A and B
Explicit learning curves for transduction and application to clustering and compression algorithms
Journal of Artificial Intelligence Research
Large margin vs. large volume in transductive learning
Machine Learning
Transductive Rademacher complexity and its applications
Journal of Artificial Intelligence Research
Transductive rademacher complexity and its applications
COLT'07 Proceedings of the 20th annual conference on Learning theory
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Predicting the labels of an unknown graph via adaptive exploration
Theoretical Computer Science
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I consider the setting of transductive learning of vertex labels in graphs, in which a graph with n vertices is sampled according to some unknown distribution; there is a true labeling of the vertices such that each vertex is assigned to exactly one of k classes, but the labels of only some (random) subset of the vertices are revealed to the learner. The task is then to find a labeling of the remaining (unlabeled) vertices that agrees as much as possible with the true labeling. Several existing algorithms are based on the assumption that adjacent vertices are usually labeled the same. In order to better understand algorithms based on this assumption, I derive data-dependent bounds on the fraction of mislabeled vertices, based on the number (or total weight) of edges between vertices differing in predicted label (i.e., the size of the cut).