Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps
Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on complex inputs x. However, these distributions are often locally miscalibrated, especially for high-stakes tail events where accurate uncertainty quantification is most needed to establish trust in models. Local miscalibration occurs because training data often lack examples of low-frequency events. The goal of this paper is to describe a simple, yet flexible framework that, at deployment, produces interpretable diagnostics and a robust correction mechanism of predictive distributions when train examples are limited. With this goal in mind, we introdu...
| Authors | Elizabeth Cucuzzella, Rafael Izbicki, Ann B. Lee |
|---|---|
| Primary category | stat.ML |
| Published | 11 March 2026 |
| Last updated at source | 21 September 2026 |
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