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Media type:
E-Article
Title:
Self‐certifying classification by linearized deep assignment
Contributor:
Boll, Bastian
[Author];
Zeilmann, Alexander
[Author];
Petra, Stefania
[Author];
Schnörr, Christoph
[Author]
Published:
Augsburg University Publication Server (OPUS), 2023
Language:
English
DOI:
https://doi.org/10.1002/pamm.202200169
Origination:
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC-Bayes risk certification paradigm. Classifiers are realized as linearly parametrized deep assignment flows with random initial conditions. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables (i) to use risk bounds as training objectives for learning posterior distributions on the hypothesis space and (ii) to compute tight out-of-sample risk certificates of randomized classifiers more efficiently than related work. Comparison with empirical test set errors illustrates the performance and practicality of this self-certifying classification method.