Derives analytical characterizations of the length-optimal training-calibration split ratio for split conformal prediction in general settings and specific regression models.
ExtremeConformalPrediction: ReliableIntervalsforHigh-Impact Events.arXiv preprint arXiv:2505.08578, 2025a
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New ADRF estimator jointly outputs tail shape, deep-tail quantities, and mean effect with an explicit refusal mechanism, claiming 11-25.5% MAE reductions on heavy-tailed data and correct refusal on insurance claims.
A survey of recent methods that apply extreme value theory to enable extrapolation in statistical learning and machine learning.
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On Optimal Data Splitting for Split Conformal Prediction
Derives analytical characterizations of the length-optimal training-calibration split ratio for split conformal prediction in general settings and specific regression models.
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Stop Suppressing the Tail: Causal Inference for Extreme Events
New ADRF estimator jointly outputs tail shape, deep-tail quantities, and mean effect with an explicit refusal mechanism, claiming 11-25.5% MAE reductions on heavy-tailed data and correct refusal on insurance claims.
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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.