Multi-class classification problem is among the most popular and well-studied statistical
frameworks. Modern multi-class datasets can be extremely ambiguous and single-output …

Artificial Intelligence (AI) methods are powerful tools for various domains, including critical
fields such as avionics, where certification is required to achieve and maintain an …

We study the problem of fair binary classification using the notion of Equal Opportunity. It
requires the true positive rate to distribute equally across the sensitive groups. Within this …

This paper presents a novel image dataset with high intrinsic ambiguity and a longtailed
distribution built from the database of Pl@ ntNet citizen observatory. It consists of 306,146 …

We propose a novel method for selective classification (SC), a problem which allows a
classifier to abstain from predicting some instances, thus trading off accuracy against …

We present a detailed study of top-$ k $ classification, the task of predicting the $ k $ most
probable classes for an input, extending beyond single-class prediction. We demonstrate …

We consider the problem of retrieving the most relevant labels for a given input when the
size of the output space is very large. Retrieval methods are modeled as set-valued …

We present a detailed study of cardinality-aware top-$ k $ classification, a novel approach
that aims to learn an accurate top-$ k $ set predictor while maintaining a low cardinality. We …

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes
instead of predicting a single class label with little guarantee. More precisely, the classifier …

Set-valued classification, a new classification paradigm that aims to identify all the plausible
classes that an observation belongs to, improves over the traditional classification …