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 …

In this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber
security. Specifically, we present cyber security threats and evaluation metrics used in the …

Multi-label classification problems usually occur in tasks related to information retrieval, like
text and image annotation, and are receiving increasing attention from the machine 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 …

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 …

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 …

In algorithmically fair prediction problems, a standard goal is to ensure the equality of
fairness metrics across multiple overlap** groups simultaneously. We reconsider this …

We develop a general approach for solving constrained classification problems, where the
loss and constraints are defined in terms of a general function of the confusion matrix. We …

Multilabel classification is rapidly develo** as an important aspect of modern predictive
modeling, motivating study of its theoretical aspects. To this end, we propose a framework …

Extreme multi-label classification (XMLC) is a task of selecting a small subset of relevant
labels from a very large set of possible labels. As such, it is characterized by long-tail labels …