Unlock: PAC-Bayes Bounds
PAC-Bayes bounds control the generalization gap of a stochastic predictor by the KL divergence between a learner-chosen posterior and a data-independent prior. They have produced some of the few non-vacuous generalization bounds reported for trained neural networks (Dziugaite-Roy 2017 onward); how tight the bound gets depends heavily on the choice of prior.
105 Prerequisites0 Mastered0 Working95 Gaps
Prerequisite mastery10%
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Inner Product Spaces and Orthogonality is your weakest prerequisite with available questions. You haven't been assessed on this topic yet.
PAC-Bayes BoundsTARGET
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