Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps
arXiv:2603.11229v2 Announce Type: replace-cross Abstract: 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 produces interpretable an
The increasing deployment of AI systems in high-stakes environments necessitates more robust and trustworthy uncertainty quantification, especially for rare but critical events.
Accurate predictive distributions for tail events are crucial for establishing trust in AI models, particularly in sectors where miscalibration can lead to significant economic or safety consequences.
The ability of AI systems to reliably forecast and quantify risk for infrequent but high-impact occurrences will be significantly enhanced, improving decision-making in critical applications.
- · Financial institutions
- · Healthcare providers
- · Defense and intelligence agencies
- · AI safety researchers
- · AI models lacking robust uncertainty quantification
- · Traditional statistical risk models
- · Systems highly reliant on point predictions
Improved model trustworthiness and broader adoption of AI in risk-sensitive domains.
Reduced human oversight requirements for certain AI-driven decision processes due to enhanced reliability.
New regulatory frameworks specifically addressing and mandating robust uncertainty quantification for AI deployments.
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Read at arXiv cs.LG