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 …

The goal of minimizing misclassification error on a training set is often just one of several
real-world goals that might be defined on different datasets. For example, one may require a …

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 …

The estimation of class prevalence, ie, of the fraction of a population that belongs to a certain
class, is an important task in data analytics, and finds applications in many domains such as …

Epilepsy is a chronic neurological disease, ranked as the second most burdensome
neurological disorder worldwide. Detecting Interictal Epileptiform Discharges (IEDs) is …

The F-measure and its variants are performance measures of choice for evaluating
classification and retrieval tasks in the presence of severe class imbalance. It is thus highly …

This work provides several fundamental characterizations of the optimal classification
function under the demographic parity constraint. In the awareness framework, akin to the …

Stochastic classifiers arise in a number of machine learning problems, and have become
especially prominent of late, as they often result from constrained optimization problems, eg …

We present a class of algorithms capable of directly training deep neural networks with
respect to popular families of task-specific performance measures for binary classification …

We present a general framework for solving a large class of learning problems with non-
linear functions of classification rates. This includes problems where one wishes to optimize …