S3ME recovers sparse causal skeletons in multivariate extremes via proxy-adjusted penalized selection and orients edges by minimizing tail prediction risk under max-linear models, with high-dimensional consistency guarantees.
arXiv preprint arXiv:2508.00223 , year =
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A survey of recent methods that apply extreme value theory to enable extrapolation in statistical learning and machine learning.
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Causal Discovery in Multivariate Extremes via Tail Asymmetry
S3ME recovers sparse causal skeletons in multivariate extremes via proxy-adjusted penalized selection and orients edges by minimizing tail prediction risk under max-linear models, with high-dimensional consistency guarantees.
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Extrapolation in Statistical Learning with Extreme Value Theory
A survey of recent methods that apply extreme value theory to enable extrapolation in statistical learning and machine learning.