Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound

Neural Information Processing Systems 

Current P AC-Bayes generalisation bounds are restricted to scalar metrics of performance, such as the loss or error rate. However, one ideally wants more information-rich certificates that control the entire distribution of possible outcomes, such as the distribution of the test loss in regression, or the probabilities of different mis-classifications.

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