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Generalization error bounds are essential to understanding machine learningalgorithms. This paper presents novel expected generalization error upperbounds based on the average joint distribution between the output hypothesisand each input training sample. Multiple generalization error upper boundsbased on different information measures are provided, including Wassersteindistance, total variation distance, KL divergence, and Jensen-Shannondivergence. Due to the convexity of the information measures, the proposedbounds in terms of Wasserstein distance and total variation distance are shownto be tighter than their counterparts based on individual samples in theliterature. An example is provided to demonstrate the tightness of the proposedgeneralization error bounds.

Tighter Expected Generalization Error Bounds via Convexity of Information Measures