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» A Guided Monte Carlo Approach to Optimization Problems
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PKDD
2009
Springer
184views Data Mining» more  PKDD 2009»
13 years 10 months ago
Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm
Abstract. This paper focuses on Active Learning with a limited number of queries; in application domains such as Numerical Engineering, the size of the training set might be limite...
Philippe Rolet, Michèle Sebag, Olivier Teyt...
ECCV
2002
Springer
14 years 7 months ago
A Markov Chain Monte Carlo Approach to Stereovision
We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the d...
Julien Sénégas
ECAI
2010
Springer
13 years 7 months ago
Bayesian Monte Carlo for the Global Optimization of Expensive Functions
In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long run...
Perry Groot, Adriana Birlutiu, Tom Heskes
ICASSP
2008
IEEE
14 years 13 days ago
Blind optimization of algorithm parameters for signal denoising by Monte-Carlo SURE
We consider the problem of optimizing the parameters of an arbitrary denoising algorithm by minimizing Stein’s Unbiased Risk Estimate (SURE) which provides a means of assessing ...
Sathish Ramani, Thierry Blu, Michael Unser
CISS
2008
IEEE
14 years 14 days ago
Near optimal lossy source coding and compression-based denoising via Markov chain Monte Carlo
— We propose an implementable new universal lossy source coding algorithm. The new algorithm utilizes two wellknown tools from statistical physics and computer science: Gibbs sam...
Shirin Jalali, Tsachy Weissman