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Generalization error bounds are critical to understanding the performance ofmachine learning models. In this work, we propose a new information-theoreticbased generalization error upper bound applicable to supervised learningscenarios. We show that our general bound can specialize in various previousbounds. We also show that our general bound can be specialized under someconditions to a new bound involving the Jensen-Shannon information between arandom variable modelling the set of training samples and another randomvariable modelling the hypothesis. We also prove that our bound can be tighterthan mutual information-based bounds under some conditions.

Jensen-Shannon Information Based Characterization of the Generalization Error of Learning Algorithms