Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss
applied to the outputs of a neural network, when the softmax is used. But, what guarantees …

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 …

We present an extensive study of surrogate losses for structured prediction supported by* $
H $-consistency bounds*. These are recently introduced guarantees that are more relevant …

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 …

A series of recent publications by Awasthi et al. have introduced the key notion of* $ H $-
consistency bounds* for surrogate loss functions. These are upper bounds on the zero-one …

Adversarial robustness is a critical property of classifiers in applications as they are
increasingly deployed in complex real-world systems. Yet, achieving accurate adversarial …

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 …

We present an extensive study of $ H $-consistency bounds for multi-class classification.
These are upper bounds on the target loss estimation error of a predictor in a hypothesis set …

We present a detailed study of $ H $-consistency bounds for score-based ranking. These
are upper bounds on the target loss estimation error of a predictor in a hypothesis set $ H …

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 …