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IJAR
2010
130views more  IJAR 2010»
13 years 6 months ago
Learning locally minimax optimal Bayesian networks
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
Tomi Silander, Teemu Roos, Petri Myllymäki
ICCV
2007
IEEE
14 years 9 months ago
Optimization and Learning for Registration of Moving Dynamic Textures
We address the problem of registering a sequence of images in a moving dynamic texture video. This involves optimization with respect to camera motion, the average image, and the ...
Junzhou Huang, Xiaolei Huang, Dimitris N. Metaxas
WSDM
2010
ACM
129views Data Mining» more  WSDM 2010»
14 years 5 months ago
Early Exit Optimizations for Additive Machine Learned Ranking Systems
Berkant Barla Cambazoglu, Hugo Zaragoza, Olivier C...
PLDI
2003
ACM
14 years 28 days ago
Meta optimization: improving compiler heuristics with machine learning
Compiler writers have crafted many heuristics over the years to approximately solve NP-hard problems efficiently. Finding a heuristic that performs well on a broad range of applic...
Mark Stephenson, Saman P. Amarasinghe, Martin C. M...
CORR
2010
Springer
125views Education» more  CORR 2010»
13 years 7 months ago
Near-Optimal Bayesian Active Learning with Noisy Observations
We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypot...
Daniel Golovin, Andreas Krause, Debajyoti Ray