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» Learning all optimal policies with multiple criteria
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PKDD
2009
Springer
184views Data Mining» more  PKDD 2009»
14 years 1 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...
COLT
2007
Springer
14 years 2 months ago
Bounded Parameter Markov Decision Processes with Average Reward Criterion
Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, t...
Ambuj Tewari, Peter L. Bartlett
ACSAC
2005
IEEE
14 years 2 months ago
Generating Policies for Defense in Depth
Coordinating multiple overlapping defense mechat differing levels of abstraction, is fraught with the potential for misconfiguration, so there is strong motivation to generate p...
Paul Rubel, Michael Ihde, Steven Harp, Charles Pay...
CORR
2010
Springer
143views Education» more  CORR 2010»
13 years 5 months ago
The Non-Bayesian Restless Multi-Armed Bandit: a Case of Near-Logarithmic Regret
In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are N arms, with rewards on all arms evolving at each time as Markov chains with known parameters. A play...
Wenhan Dai, Yi Gai, Bhaskar Krishnamachari, Qing Z...
ADBIS
2005
Springer
100views Database» more  ADBIS 2005»
14 years 2 months ago
Evolutionary Learning of Boolean Queries by Genetic Programming
Abstract. The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its...
Suhail S. J. Owais, Pavel Krömer, Václ...