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PKDD
2009
Springer
144views Data Mining» more  PKDD 2009»
16 years 1 months ago
Compositional Models for Reinforcement Learning
Abstract. Innovations such as optimistic exploration, function approximation, and hierarchical decomposition have helped scale reinforcement learning to more complex environments, ...
Nicholas K. Jong, Peter Stone
ECAI
2008
Springer
15 years 8 months ago
Exploiting locality of interactions using a policy-gradient approach in multiagent learning
In this paper, we propose a policy gradient reinforcement learning algorithm to address transition-independent Dec-POMDPs. This approach aims at implicitly exploiting the locality...
Francisco S. Melo
JMLR
2006
108views more  JMLR 2006»
15 years 6 months ago
Learning Spectral Clustering, With Application To Speech Separation
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same clu...
Francis R. Bach, Michael I. Jordan
NECO
2008
60views more  NECO 2008»
15 years 6 months ago
Sleeping Our Way to Weight Normalization and Stable Learning
The functions of sleep have been an enduring mystery. Recently, Tononi and Cirelli hypothesized that one of the functions of slow-wave sleep is to scale down synapses in the corte...
Thomas J. Sullivan, Virginia R. de Sa
ICASSP
2011
IEEE
14 years 10 months ago
A sliding-window online fast variational sparse Bayesian learning algorithm
In this work a new online learning algorithm that uses automatic relevance determination (ARD) is proposed for fast adaptive nonlinear filtering. A sequential decision rule for i...
Thomas Buchgraber, Dmitriy Shutin, H. Vincent Poor