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Proper scoring & elicitation

Which surrogate should train a verifier that can escalate?

research/reject-surrogates

Polyhedral surrogates for accept/reject/escalate verifiers: minimiser embeds the Bayes action, linear regret transfer with constant exactly 2d, logistic transfer ratio 2d(1-d)/δ, and a negative SGD result.

6 tests

What it found

Reject-option surrogates for accept/reject/escalate decisions: the generalised-hinge conditional risk embeds the Bayes action at its kinks, regret transfers linearly with constant exactly 2d (grid-verified), logistic transfer degrades as 2d(1−d)/δ, and a negative result shows logistic-plus-thresholds still learns faster in scalar SGD. Stylised.

A figure from its own output

Figure produced by research/reject-surrogates: logistic
reject-surrogates/experiments/logistic.svg, written by the project's own experiment code.

Paper, code and results

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.