A General Framework for the Practical Disintegration of PAC-Bayesian Bounds

Understand

PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers.

• However, they require a loose and costly derandomization step when applied to some families of deterministic models such as neural networks.
• As an alternative to this step, we introduce new PAC-Bayesian generalization bounds that have the originality to provide disintegrated bounds, i.e., they give guarantees over one single hypothesis instead of the usual averaged analysis.
• Our bounds are easily optimizable and can be used to design learning algorithms.