As big data becomes ubiquitous across domains, and more and more stakeholders aspire to
make the most of their data, demand for machine learning tools has spurred researchers to …

Recent successes in learning-based image classification, however, heavily rely on the large
number of annotated training samples, which may require considerable human effort. In this …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

Active learning aims to train an accurate prediction model with minimum cost by labeling
most informative instances. In this paper, we survey existing works on active learning from …

Search engines play a crucial role in our daily lives. Relevance is the core problem of a
commercial search engine. It has attracted thousands of researchers from both academia …

Active learning (AL) for object detection (OD) aims to reduce labeling costs by selecting the
most valuable samples that enhance the detection network from the unlabeled pool. Due to …

Most of the empirical evaluations of active learning approaches in the literature have
focused on a single classifier and a single performance measure. We present an extensive …

As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …

Learning-to-rank is one of the learning frameworks in machine learning and it aims to
organize the objects in a particular order according to their preference, relevance or ranking …

Multiple-instance active learning (MIAL) is a paradigm to collect sufficient training bags for a
multiple-instance learning (MIL) problem, by selecting and querying the most valuable …