Ribbon: Scalable Approximation and Robust Uncertainty Quantification
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 the first-order data-reweighting structure of the Bayesian bootstrap while requiring only post-hoc linear algebra. Ribbon approximates the Bayesian-b...
| Authors | Graham Gibson, John Tipton, Kellin Rumsey et al. |
|---|---|
| Primary category | stat.ML |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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