Self-Certifying Classification by Linearized Deep Assignment

• URL: http://arxiv.org/abs/2201.11162v1
• Date: Wed, 26 Jan 2022 19:59:14 GMT
• Title: Self-Certifying Classification by Linearized Deep Assignment
• Authors: Bastian Boll, Alexander Zeilmann, Stefania Petra, Christoph Schn\"orr
• Abstract summary: We propose a novel class of deep predictors for classifying metric data on graphs within PAC-Bayes risk certification paradigm. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables learning posterior distributions on the hypothesis space.
• Score: 65.0100925582087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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