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» Variable Selection for Optimal Decision Making
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NIPS
2008
13 years 10 months ago
Structure Learning in Human Sequential Decision-Making
We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently...
Daniel Acuña, Paul R. Schrater
ICCAD
2006
IEEE
169views Hardware» more  ICCAD 2006»
14 years 5 months ago
Microarchitecture parameter selection to optimize system performance under process variation
Abstract— Design variability due to within-die and die-todie process variations has the potential to significantly reduce the maximum operating frequency and the effective yield...
Xiaoyao Liang, David Brooks
NIPS
2007
13 years 10 months ago
What makes some POMDP problems easy to approximate?
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
David Hsu, Wee Sun Lee, Nan Rong
IJCAI
2007
13 years 10 months ago
Concept Sampling: Towards Systematic Selection in Large-Scale Mixed Concepts in Machine Learning
This paper addresses the problem of concept sampling. In many real-world applications, a large collection of mixed concepts is available for decision making. However, the collecti...
Yi Zhang 0010, Xiaoming Jin
EMO
2005
Springer
126views Optimization» more  EMO 2005»
14 years 2 months ago
The Evolution of Optimality: De Novo Programming
Abstract. Evolutionary algorithms have been quite effective in dealing with single-objective “optimization” while the area of Evolutionary Multiobjective Optimization (EMOO) h...
Milan Zeleny