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ICML
2010
IEEE
15 years 2 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
CEC
2007
IEEE
15 years 5 months ago
Adaptive farming strategies for dynamic economic environment
This paper aims to forecast the economic impacts of changing land-use in UK uplands. We assume that farmers adaptively learn and respond to a dynamic economic environment. The main...
Nanlin Jin, Mette Termansen, Klaus Hubacek, Joseph...
ICML
2003
IEEE
16 years 2 months ago
Principled Methods for Advising Reinforcement Learning Agents
An important issue in reinforcement learning is how to incorporate expert knowledge in a principled manner, especially as we scale up to real-world tasks. In this paper, we presen...
Eric Wiewiora, Garrison W. Cottrell, Charles Elkan
CORR
2006
Springer
130views Education» more  CORR 2006»
15 years 1 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...
SIGIR
2011
ACM
14 years 4 months ago
Learning to rank from a noisy crowd
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Abhimanu Kumar, Matthew Lease