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ICML
2009
IEEE
16 years 7 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
ICML
2008
IEEE
16 years 7 months ago
Empirical Bernstein stopping
Sampling is a popular way of scaling up machine learning algorithms to large datasets. The question often is how many samples are needed. Adaptive stopping algorithms monitor the ...
Csaba Szepesvári, Jean-Yves Audibert, Volod...
ICML
2005
IEEE
16 years 7 months ago
Bayesian sparse sampling for on-line reward optimization
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
WSC
2008
15 years 9 months ago
A modeling-based classification algorithm validated with simulated data
We present a Generalized Lotka-Volterra (GLV) based approach for modeling and simulation of supervised inductive learning, and construction of an efficient classification algorith...
Karen Hovsepian, Peter Anselmo, Subhasish Mazumdar
AIPS
1994
15 years 8 months ago
Control Knowledge to Improve Plan Quality
Generating production-quality plans is an essential element in transforming planners from research tools into real-world applications. However most of the work to date on learning...
M. Alicia Pérez, Jaime G. Carbonell