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» Using Gaussian Processes to Optimize Expensive Functions
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ICML
2010
IEEE
15 years 3 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
15 years 9 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»
15 years 9 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...
104
Voted
CAGD
2000
62views more  CAGD 2000»
15 years 2 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»
13 years 10 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