We study a two-stage scenario for learning to defer with multiple experts, which is crucial in
practice for many applications. In this scenario, a predictor is derived in a first stage by …

Learning with abstention is a key scenario where the learner can abstain from making a
prediction at some cost. In this paper, we analyze the score-based formulation of learning …

Automated AI classifiers should be able to defer the prediction to a human decision maker to
ensure more accurate predictions. In this work, we jointly train a classifier with a rejector …

We study the key framework of learning with abstention in the multi-class classification
setting. In this setting, the learner can choose to abstain from making a prediction with some …

Enabling machine learning classifiers to defer their decision to a downstream expert when
the expert is more accurate will ensure improved safety and performance. This objective can …

Abstract\emph {Classification with rejection}(CwR) refrains from making a prediction to avoid
critical misclassification when encountering test samples that are difficult to classify. Though …

Many practical settings allow a learner to defer predictions to one or more costly experts. For
example, the learning to defer paradigm allows a learner to defer to a human expert, at …

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 …

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 …

Classifying patterns of known classes and rejecting ambiguous and novel (also called as out-
of-distribution (OOD)) inputs are involved in open world pattern recognition. Deep neural …