  Generalization error bounds are critical to understanding the performance of
machine learning models. In this work, we propose a new information-theoretic
based generalization error upper bound applicable to supervised learning
scenarios. We show that our general bound can specialize in various previous
bounds. We also show that our general bound can be specialized under some
conditions to a new bound involving the Jensen-Shannon information between a
random variable modelling the set of training samples and another random
variable modelling the hypothesis. We also prove that our bound can be tighter
than mutual information-based bounds under some conditions.
