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» Approximation Algorithms for Min-Max Generalization Problems
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ISAAC
2004
Springer
107views Algorithms» more  ISAAC 2004»
14 years 2 months ago
On the Hardness and Easiness of Random 4-SAT Formulas
Assuming 3-SAT formulas are hard to refute with high probability, Feige showed approximation hardness results, among others for the max bipartite clique. We extend this result in t...
Andreas Goerdt, André Lanka
SODA
2010
ACM
209views Algorithms» more  SODA 2010»
14 years 6 months ago
Counting Stars and Other Small Subgraphs in Sublinear Time
Detecting and counting the number of copies of certain subgraphs (also known as network motifs or graphlets), is motivated by applications in a variety of areas ranging from Biolo...
Mira Gonen, Dana Ron, Yuval Shavitt
STOC
2003
ACM
152views Algorithms» more  STOC 2003»
14 years 9 months ago
Reducing truth-telling online mechanisms to online optimization
We describe a general technique for converting an online algorithm B to a truthtelling mechanism. We require that the original online competitive algorithm has certain "nicen...
Baruch Awerbuch, Yossi Azar, Adam Meyerson
FOCS
2003
IEEE
14 years 2 months ago
Clustering with Qualitative Information
We consider the problem of clustering a collection of elements based on pairwise judgments of similarity and dissimilarity. Bansal, Blum and Chawla (in: Proceedings of 43rd FOCS, ...
Moses Charikar, Venkatesan Guruswami, Anthony Wirt...
ICML
2005
IEEE
14 years 9 months ago
Predictive low-rank decomposition for kernel methods
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Francis R. Bach, Michael I. Jordan