论文标题
PAC $^M $ -BAYES：缩小错误指定的贝叶斯政权的经验风险差距
PAC$^m$-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime
论文作者
论文摘要
贝叶斯后部将“推论风险”最小化，该风险本身限制了“预测风险”。当可能性和先验指定的可能性良好时，这种界限就很紧。但是，由于错误指定会导致差距，因此贝叶斯后验预测分布可能具有较差的泛化性能。这项工作产生了多样本损失（PAC $^m $），可以通过跨越两个风险之间的权衡来缩小差距。损失在计算上是有利的，并提供了PAC泛化保证。经验研究表明预测分布有所改善。
The Bayesian posterior minimizes the "inferential risk" which itself bounds the "predictive risk". This bound is tight when the likelihood and prior are well-specified. However since misspecification induces a gap, the Bayesian posterior predictive distribution may have poor generalization performance. This work develops a multi-sample loss (PAC$^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.
