Workshop: Your Model is Wrong: Robustness and misspecification in probabilistic modeling

PAC^m-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime

Joshua Dillon · Warren Morningstar · Alexander Alemi


The Bayesian posterior minimizes the "inferential risk" which itself bounds the "predictive risk." This bound is tight when the likelihood and prior are well-specified. However since misspecification induces a gap, the Bayesian posterior predictive distribution may have poor generalization performance. This work develops a multi-sample loss (PAC^m) which can close the gap by spanning a trade-off between the two risks. The loss is computationally favorable and offers PAC generalization guarantees. Empirical study demonstrates improvement to the predictive distribution.

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