Extrapolation in Statistical Learning with Extreme Value Theory
Extreme value theory provides rigorous theory and statistical tools for extrapolation in machine learning, particularly in settings where traditional methods struggle due to data scarcity in the tails. A broad range of tasks benefit from these advances, including regression and classification beyond the training data, extreme quantile regression, supervised and unsupervised dimension reduction, generative artificial intelligence and anomaly detection. This review synthesizes recent developments in these fields at the intersection of statistical learning and extreme value theory, with a focus on principled methods based on asymptotically motivated representations of the tail of univariate ...
| Authors | Sebastian Engelke, Nicola Gnecco, Anne Sabourin |
|---|---|
| Primary category | stat.ML |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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