Principled Approaches for Learning to Defer with Multiple Experts

arXiv:2310.14774v2 Announce Type: replace
Abstract: We present a study of surrogate losses and algorithms for the general problem of learning to defer with multiple experts. We first introduce a new family of surrogate losses specifically tailored for the multiple-expert setting, where the prediction and deferral functions are learned simultaneously. We then prove that these surrogate losses benefit from strong $H$-consistency bounds. We illustrate the application of our analysis through several examples of practical surrogate losses, for which we give explicit …

abstract algorithms arxiv cs.lg expert experts family functions general losses multiple prediction stat.ml study type