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896views
15 years 1 months ago
Exponential families and simplification of mixture models
Presentation of the exponential families, of the mixtures of such distributions and how to learn it. We then present algorithms to simplify mixture model, using Kullback-Leibler di...
ATAL
2008
Springer
15 years 6 months ago
On the usefulness of opponent modeling: the Kuhn Poker case study
The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state inform...
Alessandro Lazaric, Mario Quaresimale, Marcello Re...
ICML
2009
IEEE
16 years 5 months ago
Near-Bayesian exploration in polynomial time
We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to model-based RL offers an elegant solution to this problem, by considering...
J. Zico Kolter, Andrew Y. Ng
KDD
2004
ACM
154views Data Mining» more  KDD 2004»
16 years 4 months ago
Diagnosing extrapolation: tree-based density estimation
There has historically been very little concern with extrapolation in Machine Learning, yet extrapolation can be critical to diagnose. Predictor functions are almost always learne...
Giles Hooker
CIKM
2011
Springer
14 years 4 months ago
Citation count prediction: learning to estimate future citations for literature
In most of the cases, scientists depend on previous literature which is relevant to their research fields for developing new ideas. However, it is not wise, nor possible, to trac...
Rui Yan, Jie Tang, Xiaobing Liu, Dongdong Shan, Xi...