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Ribbon: Scalable Approximation and Robust Uncertainty Quantification

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arXiv:2606. 27269v1 Announce Type: new Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models.

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Authors: Graham Gibson, John Tipton, Kellin Rumsey, Natalie Klein
Released: 2026-06-26 (12d ago)
Topic: Related to current project
Relation to my work: Read next because Ribbon: Scalable Approximation and Robust Uncertainty Quantification overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)". Matching terms: class, rect, under, correct, line, rate, full, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.27269

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