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» Empirical Design of Geometric Algorithms
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WSC
2000
15 years 1 months ago
Variance reduction techniques for value-at-risk with heavy-tailed risk factors
The calculation of value-at-risk (VAR) for large portfolios of complex instruments is among the most demanding and widespread computational challenges facing the financial industr...
Paul Glasserman, Philip Heidelberger, Perwez Shaha...
JAIR
2006
101views more  JAIR 2006»
14 years 11 months ago
Resource Allocation Among Agents with MDP-Induced Preferences
Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute acti...
Dmitri A. Dolgov, Edmund H. Durfee
TEC
2002
128views more  TEC 2002»
14 years 11 months ago
A framework for evolutionary optimization with approximate fitness functions
It is not unusual that an approximate model is needed for fitness evaluation in evolutionary computation. In this case, the convergence properties of the evolutionary algorithm are...
Yaochu Jin, Markus Olhofer, Bernhard Sendhoff
ALT
2001
Springer
15 years 8 months ago
Learning of Boolean Functions Using Support Vector Machines
This paper concerns the design of a Support Vector Machine (SVM) appropriate for the learning of Boolean functions. This is motivated by the need of a more sophisticated algorithm ...
Ken Sadohara
91
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ATAL
2007
Springer
15 years 6 months ago
Combinatorial resource scheduling for multiagent MDPs
Optimal resource scheduling in multiagent systems is a computationally challenging task, particularly when the values of resources are not additive. We consider the combinatorial ...
Dmitri A. Dolgov, Michael R. James, Michael E. Sam...