Data-dependent PAC-Bayes priors via differential privacy

NeurIPS 2018 Gintare Karolina DziugaiteDaniel M. Roy

The Probably Approximately Correct (PAC) Bayes framework (McAllester, 1999) can incorporate knowledge about the learning algorithm and (data) distribution through the use of distribution-dependent priors, yielding tighter generalization bounds on data-dependent posteriors. Using this flexibility, however, is difficult, especially when the data distribution is presumed to be unknown... (read more)

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