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» Terminating Decision Algorithms Optimally
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IJCAI
2007
15 years 4 months ago
Using Linear Programming for Bayesian Exploration in Markov Decision Processes
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
Pablo Samuel Castro, Doina Precup
ANOR
2010
112views more  ANOR 2010»
15 years 1 months ago
Online stochastic optimization under time constraints
This paper considers online stochastic optimization problems where uncertainties are characterized by a distribution that can be sampled and where time constraints severely limit t...
Pascal Van Hentenryck, Russell Bent, Eli Upfal
KDD
2005
ACM
142views Data Mining» more  KDD 2005»
16 years 3 months ago
Towards exploratory test instance specific algorithms for high dimensional classification
In an interactive classification application, a user may find it more valuable to develop a diagnostic decision support method which can reveal significant classification behavior...
Charu C. Aggarwal
ANOR
2007
165views more  ANOR 2007»
15 years 3 months ago
Financial scenario generation for stochastic multi-stage decision processes as facility location problems
The quality of multi-stage stochastic optimization models as they appear in asset liability management, energy planning, transportation, supply chain management, and other applicat...
Ronald Hochreiter, Georg Ch. Pflug
COMPGEOM
2010
ACM
15 years 8 months ago
Optimal reconstruction might be hard
Sampling conditions for recovering the homology of a set using topological persistence are much weaker than sampling conditions required by any known algorithm for producing a top...
Dominique Attali, André Lieutier