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arXiv Machine Learning By Laura Iacovissi, Rabanus Derr, Robert C. Williamson

Comparing Corrupted Constrained Learning Problems

Read the original on arXiv Machine Learning →

The paper discusses the data processing inequality (DPI) in statistics, which states that a stochastically modified experiment cannot have a lower Bayes risk than the original. It shows that this classical DPI does not hold for constrained learning problems common in machine learning, where the model class is limited. The authors propose a generalized DPI that applies to constrained Bayes risks, linking it to a set containment condition on a superprediction set, and provide sufficient conditions for this containment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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