Uncertainty quantification via cross-validation and its variants under algorithmic stability

Understand

Recently, there has been substantial interest in statistical guarantees for cross-validation (CV) methods of uncertainty quantification in statistical learning (cf.

• Barber et al.
• 2021a, Liang and Barber 2024, Steinberger and Leeb 2023).
• These guarantees should hold under minimal assumptions on the data generating process and conditional on the training data, because numerous predictions are usually computed based on one and the same training sample.