Tighter Expected Generalization Error Bounds via Wasserstein DistanceDownload PDF

21 May 2021, 20:50 (edited 25 Jan 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: generalization error, wasserstein distance
  • TL;DR: New, tighter generalization error bounds based on the Wasserstein distance that recover from below previous bounds based on the relative entropy and generate several new ones.
  • Abstract: This work presents several expected generalization error bounds based on the Wasserstein distance. More specifically, it introduces full-dataset, single-letter, and random-subset bounds, and their analogous in the randomized subsample setting from Steinke and Zakynthinou [1]. Moreover, when the loss function is bounded and the geometry of the space is ignored by the choice of the metric in the Wasserstein distance, these bounds recover from below (and thus, are tighter than) current bounds based on the relative entropy. In particular, they generate new, non-vacuous bounds based on the relative entropy. Therefore, these results can be seen as a bridge between works that account for the geometry of the hypothesis space and those based on the relative entropy, which is agnostic to such geometry. Furthermore, it is shown how to produce various new bounds based on different information measures (e.g., the lautum information or several $f$-divergences) based on these bounds and how to derive similar bounds with respect to the backward channel using the presented proof techniques.
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