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» Using Gaussian Processes to Optimize Expensive Functions
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ICML
2010
IEEE
13 years 10 months ago
Bayesian Multi-Task Reinforcement Learning
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Alessandro Lazaric, Mohammad Ghavamzadeh
IPSN
2009
Springer
14 years 3 months ago
Approximating sensor network queries using in-network summaries
In this work we present new in-network techniques for communication efficient approximate query processing in wireless sensornets. We use a model-based approach that constructs a...
Alexandra Meliou, Carlos Guestrin, Joseph M. Helle...
LION
2009
Springer
210views Optimization» more  LION 2009»
14 years 3 months ago
Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows
Beam-ACO algorithms are hybrid methods that combine the metaheuristic ant colony optimization with beam search. They heavily rely on accurate and computationally inexpensive boundi...
Manuel López-Ibáñez, Christia...
CAGD
2000
62views more  CAGD 2000»
13 years 8 months ago
Volume morphing and rendering - An integrated approach
In this paper, we first introduce a 3D morphing method for landmark-based volume deformation, using various scattered data interpolation schemes. Qualitative and speed comparisons...
Shiaofen Fang, Rajagopalan Srinivasan, Raghu Ragha...
CORR
2012
Springer
235views Education» more  CORR 2012»
12 years 4 months ago
An Incremental Sampling-based Algorithm for Stochastic Optimal Control
Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation ...
Vu Anh Huynh, Sertac Karaman, Emilio Frazzoli