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» Terminating Decision Algorithms Optimally
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GECCO
2006
Springer
161views Optimization» more  GECCO 2006»
15 years 7 months ago
Innovization: innovating design principles through optimization
This paper introduces a new design methodology (we call it "innovization") in the context of finding new and innovative design principles by means of optimization techni...
Kalyanmoy Deb, Aravind Srinivasan
ICCD
2006
IEEE
171views Hardware» more  ICCD 2006»
16 years 13 days ago
Stochastic Dynamic Thermal Management: A Markovian Decision-based Approach
This paper proposes a stochastic dynamic thermal management (DTM) technique in high-performance VLSI system with especial attention to the uncertainty in temperature observation. ...
Hwisung Jung, Massoud Pedram
138
Voted
ICML
1994
IEEE
15 years 7 months ago
Markov Games as a Framework for Multi-Agent Reinforcement Learning
In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function....
Michael L. Littman
116
Voted
ICML
2010
IEEE
15 years 4 months ago
Risk minimization, probability elicitation, and cost-sensitive SVMs
A new procedure for learning cost-sensitive SVM classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the cost-sensitive SVM is derived as the...
Hamed Masnadi-Shirazi, Nuno Vasconcelos
IPPS
2002
IEEE
15 years 8 months ago
Optimal Remapping in Dynamic Bulk Synchronous Computations via a Stochastic Control Approach
A bulk synchronous computation proceeds in phases that are separated by barrier synchronization. For dynamic bulk synchronous computations that exhibit varying phase-wise computat...
Gang George Yin, Cheng-Zhong Xu, Le Yi Wang