mlsanityData to 7 Oct 2026

From Generalisation Error to Transportation-cost Inequalities and Back

arXiv 2202.03956 ↗First posted 8 Feb 2022cs.IT cs.LG math.FAcited by 6

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Abstract (arXiv)

In this work, we connect the problem of bounding the expected generalisation error with transportation-cost inequalities. Exposing the underlying pattern behind both approaches we are able to generalise them and go beyond Kullback-Leibler Divergences/Mutual Information and sub-Gaussian measures. In particular, we are able to provide a result showing the equivalence between two families of inequalities: one involving functionals and one involving measures. This result generalises the one proposed by Bobkov and G"otze that connects transportation-cost inequalities with concentration of measure. Moreover, it allows us to recover all standard generalisation error bounds involving mutual information and to introduce new, more general bounds, that involve arbitrary divergence measures.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 8 Feb 2022.

Builds on

5 of 16 references.

References · 5

  1. 2017Information-theoretic analysis of generalization capability of learning algorithmscited by 299
  2. 2020Reasoning About Generalization via Conditional Mutual Informationcited by 145
  3. 2019Tightening Mutual Information Based Bounds on Generalization Errorcited by 117
  4. 2018Generalization Error Bounds for Noisy, Iterative Algorithmscited by 79
  5. 2019Generalization Error Bounds Via R'enyi-, f-Divergences and Maximal Leakagecited by 48

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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1.

6 papers.