We consider multi-label crowdsourcing learning in two scenarios. In the first scenario, we
aim at inferring instances' groundtruth given the crowds' annotations. We propose two …

Crowdsourcing learning, in which labels are collected from multiple workers through
crowdsourcing platforms, has attracted much attention during the past decade. This learning …

Non-expert annotators must select an appropriate label for an image when the annotation
task is difficult. Then, it might be easier for an annotator to choose multiple “likely” labels …

Many annotation systems provide to add an unsure option in the labels, because the
annotators have different expertise, and they may not have enough confidence to choose a …

In the context of the policy debate around business practices related to the marketing of
branded food products as being identical (ie in their brand and appearance on the …

This thesis focuses on utilising and adapting active learning pipelines to address the
complexities of real-world scientific datasets and optimising for the constraints of the human …

In many Information Retrieval tasks, the boundary between classes is not well defined, and
assigning a document to a specific class may be complicated, even for humans. For …

With the abundance of industrial datasets, imbalanced classification has become a common
problem in several application domains. Oversampling is an effective method to solve …

Modern machine learning is migrating to the era of complex models, which requires a
plethora of well-annotated data. While crowdsourcing is a promising tool to achieve this …

Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in
the traditional case annotators are asked to provide a single label for each instance, novel …