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PAC-Bayesian learning methods combine the informative priors of Bayesian methods with distribution-free PAC guarantees. Stochastic model selection predicts a class label by stochastically sampling a classifier according to a “posterior distribution” on classifiers. This paper gives a PAC-Bayesian performance guarantee for stochastic model selection that is superior to analogous guarantees for deterministic...
This paper gives PAC guarantees for “Bayesian” algorithms—algorithms that optimize risk minimization expressions involving a prior probability and a likelihood for the training data. PAC-Bayesian algorithms are motivated by a desire to provide an informative prior encoding information about the expected experimental setting but still having PAC performance guarantees over all IID settings. The PAC-Bayesian...
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