Generalization error bounds using Wasserstein distances.
ITW, pp.1-5, (2018)
Generalization error of a learning algorithm characterizes the gap between an algorithm’s performance on test data versus performance on training data. In recent work, Xu u0026 Raginsky  showed that generalization error may be upper- bounded using the mutual information $I(S;W)$ between the input $S$ and the output $W$ of an algorithm....More
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