Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

In supervised learning, acquiring labeled training data for a predictive model can be very
costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is …

Active learning (AL) is a machine-learning approach for reducing the data labeling effort.
Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a …

We state and analyze the first active learning algorithm which works in the presence of
arbitrary forms of noise. The algorithm, A 2 (for Agnostic Active), relies only upon the …

Active learning is well-motivated in many supervised learning tasks where unlabeled data
may be abundant but labeled examples are expensive to obtain. The goal of active learning …

Automatic image aesthetics rating has received a growing interest with the recent
breakthrough in deep learning. Although many studies exist for learning a generic or …

Mathematical models are widely used in many science disciplines, such as physics, biology
and meteorology. They are aimed at better understanding and explaining real-world …

Empirical evidence shows that in favorable situations semi-supervised learning (SSL)
algorithms can capitalize on the abundancy of unlabeled training data to improve the …

Multiple lines of evidence suggest that predictive models may benefit from algorithmic triage.
Under algorithmic triage, a predictive model does not predict all instances but instead defers …

When monitoring spatial phenomena, such as the ecological condition of a river, deciding
where to make observations is a challenging task. In these settings, a fundamental question …