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SCP
2000
119views more  SCP 2000»
13 years 8 months ago
Automated compositional Markov chain generation for a plain-old telephone system
Obtaining performance models, like Markov chains and queueing networks, for systems of significant complexity and magnitude is a difficult task that is usually tackled using human...
Holger Hermanns, Joost-Pieter Katoen
ICML
2009
IEEE
14 years 9 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
IOR
2011
152views more  IOR 2011»
13 years 3 months ago
Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition
We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as ...
Naomi Miller, Andrzej Ruszczynski
ICML
2008
IEEE
14 years 9 months ago
Efficiently learning linear-linear exponential family predictive representations of state
Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of...
David Wingate, Satinder P. Singh
FMCO
2009
Springer
161views Formal Methods» more  FMCO 2009»
13 years 6 months ago
The How and Why of Interactive Markov Chains
This paper reviews the model of interactive Markov chains (IMCs, for short), an extension of labelled transition systems with exponentially delayed transitions. We show that IMCs a...
Holger Hermanns, Joost-Pieter Katoen