Multi-label learning deals with problems where each example is represented by a single
instance while being associated with multiple class labels simultaneously. Binary relevance …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

River is a machine learning library for dynamic data streams and continual learning. It
provides multiple state-of-the-art learning methods, data generators/transformers …

We develop a first line of attack for solving programming competition-style problems from
input-output examples using deep learning. The approach is to train a neural network to …

Machine learning classifiers for human-facing tasks such as comment toxicity and
misinformation often score highly on metrics such as ROC AUC but are received poorly in …

In this paper, we propose a novel deep learning architecture for multi-label zero-shot
learning (ML-ZSL), which is able to predict multiple unseen class labels for each input …

Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

Abstract Concept bottleneck models (CBMs) enhance the interpretability of their predictions
by first predicting high-level concepts given features, and subsequently predicting outcomes …