The key idea behind active learning is that a machine learning algorithm can achieve
greater accuracy with fewer labeled training instances if it is allowed to choose the training …

This article surveys recent work in active learning aimed at making it more practical for real-
world use. In general, active learning systems aim to make machine learning more …

We propose a new active learning strategy designed for deep neural networks. The goal is
to minimize the number of data annotation queried from an oracle during training. Previous …

Reinforcement learning (RL) requires access to a reward function that incentivizes the right
behavior, but these are notoriously hard to specify for complex tasks. Preference-based RL …

Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …

As a way to relieve the tedious work of manual annotation, active learning plays important
roles in many applications of visual concept recognition. In typical active learning scenarios …

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of
convenient computational tools. Here, we describe METIS, a versatile active machine …

Attributed networks are pervasive in different domains, ranging from social networks, gene
regulatory networks to financial transaction networks. This kind of rich network …

Most active learning approaches select either informative or representative unlabeled
instances to query their labels. Although several active learning algorithms have been …

Abstract Information-theoretic approaches to active learning have traditionally focused on
maximising the information gathered about the model parameters, most commonly by …