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» The Computational Structure of Progress Conditions
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IJCAI
2001
14 years 11 months ago
Balance and Filtering in Structured Satisfiable Problems
New methods to generate hard random problem instances have driven progress on algorithms for deduction and constraint satisfaction. Recently Achlioptas et al. (AAAI 2000) introduc...
Henry A. Kautz, Yongshao Ruan, Dimitris Achlioptas...
BMCBI
2010
147views more  BMCBI 2010»
14 years 9 months ago
Learning biological network using mutual information and conditional independence
Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a revers...
Dong-Chul Kim, Xiaoyu Wang, Chin-Rang Yang, Jean G...
JMLR
2008
230views more  JMLR 2008»
14 years 9 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
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SIROCCO
2008
14 years 11 months ago
Sharpness: A Tight Condition for Scalability
: A distributed system is scalable if the rate at which it completes its computation and communication tasks does not depend on its size. As an example, the scalability of a peer-t...
Augustin Chaintreau
IJAR
2007
69views more  IJAR 2007»
14 years 9 months ago
Racing algorithms for conditional independence inference
In this article, we consider the computational aspects of deciding whether a conditional independence statement t is implied by a list of conditional independence statements L usi...
Remco R. Bouckaert, Milan Studený