ICML Conference 2008 Conference Paper
Actively learning level-sets of composite functions
- Brent Bryan
- Jeff G. Schneider
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ICML Conference 2008 Conference Paper
ICML Conference 2007 Conference Paper
NeurIPS Conference 2005 Conference Paper
We present an efficient algorithm to actively select queries for learning the boundaries separating a function domain into regions where the func- tion is above and below a given threshold. We develop experiment selec- tion methods based on entropy, misclassification rate, variance, and their combinations, and show how they perform on a number of data sets. We then show how these algorithms are used to determine simultaneously valid 1 − α confidence intervals for seven cosmological parameters. Ex- perimentation shows that the algorithm reduces the computation neces- sary for the parameter estimation problem by an order of magnitude.