Consistent Learning-to-Defer with Expert-Conditional Advice
We introduce learning-to-defer with advice, a new setting in which a system learns both which expert should handle an input and which information that expert should receive. The policy acts before observing advice and minimizes prediction loss plus expert and acquisition fees. Training may reveal all advice sources or only a subset on each record. We show that composing routing and advice losses can be inconsistent and that augmented training can incur avoidable estimation variance. We propose reference conditional classification, which jointly learns a fixed expert's error probability and each action's error conditional on that expert's success or failure. The reference outcome is observ...
| Authors | Yannis Montreuil, Leïna Montreuil, Axel Carlier et al. |
|---|---|
| Primary category | stat.ML |
| Published | 15 March 2026 |
| Last updated at source | 5 October 2026 |
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