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» Learning Bounds for Domain Adaptation
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ECTEL
2010
Springer
13 years 7 months ago
GVIS: A Facility for Adaptively Mashing Up and Representing Open Learner Models
In this article we present an infrastructure for creating mash up and visual representations of the user profile that combine data from different sources. We explored this approach...
Luca Mazzola, Riccardo Mazza
VMV
2008
151views Visualization» more  VMV 2008»
13 years 11 months ago
Adaptive surface decomposition for the distance computation of arbitrarily shaped objects
We propose an adaptive decomposition algorithm to compute separation distances between arbitrarily shaped objects. Using the Gilbert-JohnsonKeerthi algorithm (GJK), we search for ...
Marc Gissler, Matthias Teschner
ICML
2008
IEEE
14 years 11 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...
ATAL
2008
Springer
14 years 2 days ago
A few good agents: multi-agent social learning
In this paper, we investigate multi-agent learning (MAL) in a multi-agent resource selection problem (MARS) in which a large group of agents are competing for common resources. Si...
Jean Oh, Stephen F. Smith
COLT
2008
Springer
13 years 12 months ago
High-Probability Regret Bounds for Bandit Online Linear Optimization
We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ( ...
Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, S...