Comparing Corrupted Constrained Learning Problems
A key result in statistics is the data processing inequality, originally proved by Blackwell (1951) and later refined by DeGroot (1962) in terms of statistical uncertainty. The latter statement claims that the Bayes risk achieved on raw data always undercuts the Bayes risk on a processed version of the same data. This seemingly contradicts empirical findings in machine learning: pre-training, representation learning, feature learning, data augmentation are techniques used to improve performance of machine learning models. We reconcile both worlds by simply accepting that machine learning problems are constrained learning problems: the model class used does not include all measurable funct...
| Authors | Laura Iacovissi, Rabanus Derr, Robert C. Williamson |
|---|---|
| Primary category | cs.LG |
| Published | 26 August 2026 |
| Last updated at source | 29 September 2026 |
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