PAC-Bayes Analysis Beyond the Usual Bounds

23 Jun 2020Omar RivasplataIlja KuzborskijCsaba SzepesvariJohn Shawe-Taylor

We focus on a stochastic learning model where the learner observes a finite set of training examples and the output of the learning process is a data-dependent distribution over a space of hypotheses. The learned data-dependent distribution is then used to make randomized predictions, and the high-level theme addressed here is guaranteeing the quality of predictions on examples that were not seen during training, i.e. generalization... (read more)

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