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» Stochastic complexity in learning
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ICALP
2000
Springer
15 years 5 months ago
A Matrix-based Method for Analysing Stochastic Process Algebras
This paper demonstrates how three stochastic process algebras can be mapped on to a generally-distributed stochastic transition system. We demonstrate an aggregation technique on ...
Jeremy T. Bradley, N. J. Davies
133
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UAI
2008
15 years 3 months ago
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
Emma Brunskill, Bethany R. Leffler, Lihong Li, Mic...
CVPR
2007
IEEE
16 years 3 months ago
Mapping Natural Image Patches by Explicit and Implicit Manifolds
Image patches are fundamental elements for object modeling and recognition. However, there has not been a panoramic study of the structures of the whole ensemble of natural image ...
Kent Shi, Song Chun Zhu
ICML
2009
IEEE
16 years 2 months ago
Model-free reinforcement learning as mixture learning
We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
Nikos Vlassis, Marc Toussaint
129
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CORR
2008
Springer
208views Education» more  CORR 2008»
15 years 1 months ago
Equilibria, Fixed Points, and Complexity Classes
Many models from a variety of areas involve the computation of an equilibrium or fixed point of some kind. Examples include Nash equilibria in games; market equilibria; computing o...
Mihalis Yannakakis