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Aggregation via Empirical risk minimization

Mendelson, Shahar; Lecue, Guillaume


Given a finite set F of estimators, the problem of aggregation is to construct a new estimator whose risk is as close as possible to the risk of the best estimator in F. It was conjectured that empirical minimization performed in the convex hull of F is an optimal aggregation method, but we show that this conjecture is false. Despite that, we prove that empirical minimization in the convex hull of a well chosen, empirically determined subset of F is an optimal aggregation method.

CollectionsANU Research Publications
Date published: 2009
Type: Journal article
Source: Probability Theory and Related Fields
DOI: 10.1007/s00440-008-0180-8


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