Actively Learning Level-Sets of Composite Functions (2008)

Abstract

Scientists frequently have multiple types of experiments and data sets
on which they can test the validity of their parameterized models and
locate plausible regions for the model parameters. By examining
multiple data sets, scientists can obtain inferences which typically
are much more informative than the deductions derived from each of the
data sources independently. Several standard data combination
techniques result in target functions which are a weighted sum of the
observed data sources. Thus, computing constraints on the plausible regions
of the model parameter space can be formulated as finding a
level set of a target function which is the sum of observable functions.
We propose an active learning algorithm for this problem which
selects both a sample (from the parameter space) and an observable function
upon which to compute the next sample. Empirical tests on synthetic
functions and on real data for an eight parameter cosmological model
show that our algorithm significantly reduces the number of samples
required to identify the desired level-set.

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Approximate BibTeX Entry

@inproceedings{bryan_icml2008,
    Month = {July},
    Year = {2008},
    Publisher = {ACM},
    Booktitle = {ICML 2008: Proceedings of the 25th International Conference on Machine Learning},
    Author = { Brent Bryan, Jeff Schneider },
    Title = {Actively Learning Level-Sets of Composite Functions}
}