Poster
in
Workshop: Bayesian Decision-making and Uncertainty: from probabilistic and spatiotemporal modeling to sequential experiment design
Learning to Defer with an Uncertain Rejector via Conformal Prediction
Yizirui Fang · Eric Nalisnick
Keywords: [ conformal prediction ] [ Uncertain Quantification ] [ safety ] [ learning to defer ]
We perform uncertainty quantification for the rejector sub-component of the learning-to-defer framework. In particular, we use conformal prediction to allow the reject to output sets, instead of just the binary outcome of 'defer' or not. Our method builds on the existing L2D framework, which previously optimized a specific surrogate loss, namely the one-over-all loss. We evaluate our approach by reporting abstention and consensus prediction results on the CIFAR-10 dataset, demonstrating improvements over the traditional method.