arXiv:2606.27269v1 Announce Type: cross Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models. Both fully Bayesian and bootstrap resampling methods provide principled uncertainty estimates but are often too expensive for modern machine-learning models because they require posterior sampling or repeated model refitting. We introduce Ribbon, a scalable approximation to Dirichlet-reweighted bootstrap uncertainty. Ribbon replaces repeated refitting with an influence-function linearization around a single fitted model, preserving th
The increasing complexity and scale of modern machine learning models necessitate more efficient methods for uncertainty quantification, driving innovation in this area.
Reliable and scalable uncertainty quantification is critical for deploying AI safely and effectively in high-stakes environments, enabling better decision-making and trust.
This advancement proposes a method to achieve robust uncertainty estimates at a significantly lower computational cost, making such capabilities accessible to a wider range of AI applications.
- · AI developers
- · High-stakes AI applications
- · Cloud computing providers
- · Data scientists
- · Legacy uncertainty quantification methods
- · Models requiring extensive re-training
Reduced computational burden for AI model development and deployment involving uncertainty quantification.
Faster iteration cycles for AI model training and validation, accelerating AI research and product development.
Broader adoption of AI in sensitive sectors due to improved trust and interpretability through reliable uncertainty estimates.
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Read at arXiv cs.LG