Self-Certifying Classification by Linearized Deep Assignment [article]

Bastian Boll, Alexander Zeilmann, Stefania Petra, Christoph Schnörr
2022 arXiv   pre-print
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
more » ... ates 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.
arXiv:2201.11162v2 fatcat:yzo4mlahf5gydfkj6qbhfkxuty