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Pascal Germain
PAC-Bayesian Theory Meets Bayesian Inference
Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien
That is, for the negative log-likelihood loss function, we show that the minimization of PAC-Bayesian generalization risk bounds maximizes the Bayesian marginal likelihood. This provides an alternative explanation to the Bayesian Occam's razor criteria, under the assumption that the data is generated by an i.i.d.