Calibrated Learning to Defer with One-vs-All Classifiers

Catalog evidence and recorded chart appearances. Inclusion is not an endorsement.

Source snapshot imported 2026-10-20. This is not a live feed. Source and contributions.

Evidence and limits

Finding: A prior consistent deferral formulation was not calibrated for expert correctness; the proposed one-vs-all method produced calibrated probabilities in tested tasks.

Impact: Strengthen calibration testing for deferral estimates. Reject consistency or accuracy alone as calibrated routing evidence.

Boundary: Classification benchmarks do not validate open-ended delegation. Next: what calibration target is meaningful when correctness is delayed or multidimensional?

Flags: classification benchmarks; delayed outcomes absent

Record State: Catalog review recorded; not a performance endorsement

What was reviewed: Structure and relevance screened; not a full evaluation

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Recorded chart appearances

No appearance in the chart editions included in this snapshot. This is not evidence of absence from other editions.