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stat.ML2022★ 2 cited
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles
Rajeev Verma, Daniel Barrejón, Eric Nalisnick
We study the statistical properties of learning to defer (L2D) to multiple experts. In particular, we address the open problems of deriving a consistent surrogate loss, confidence…
cs.LG2022★ 3 cited
Calibrated Learning to Defer with One-vs-All Classifiers
Rajeev Verma, Eric Nalisnick
The learning to defer (L2D) framework has the potential to make AI systems safer. For a given input, the system can defer the decision to a human if the human is more likely than t…