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UAI
2003
13 years 8 months ago
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the E...
Changhe Yuan, Marek J. Druzdzel
SIAMCO
2000
117views more  SIAMCO 2000»
13 years 7 months ago
The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
Vivek S. Borkar, Sean P. Meyn
CP
2004
Springer
14 years 26 days ago
Heuristic Selection for Stochastic Search Optimization: Modeling Solution Quality by Extreme Value Theory
The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling ...
Vincent A. Cicirello, Stephen F. Smith
WSC
2001
13 years 8 months ago
Global random optimization by simultaneous perturbation stochastic approximation
We examine the theoretical and numerical global convergence properties of a certain "gradient free" stochastic approximation algorithm called the "simultaneous pertu...
John L. Maryak, Daniel C. Chin
SIGECOM
2011
ACM
232views ECommerce» more  SIGECOM 2011»
12 years 10 months ago
Near optimal online algorithms and fast approximation algorithms for resource allocation problems
We present algorithms for a class of resource allocation problems both in the online setting with stochastic input and in the offline setting. This class of problems contains man...
Nikhil R. Devanur, Kamal Jain, Balasubramanian Siv...