Supervised machine learning methods are increasingly employed in political science. Such
models require costly manual labeling of documents. In this paper, we introduce active …

Training data creation is increasingly a key bottleneck for develo** machine learning,
especially for deep learning systems. Active learning provides a cost-effective means for …

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 …

Plan recognition, activity recognition, and intent recognition together combine and unify
techniques from user modeling, machine vision, intelligent user interfaces, human/computer …

The ability to compute an accurate reward function is essential for optimising a dialogue
policy via reinforcement learning. In real-world applications, using explicit user feedback as …

Systems that automatically recognize human activities offer the potential of timely, task-
relevant information and support. For example, prompting systems can help keep people …

With crowdsourcing systems, it is easy to collect multiple noisy labels for the same object for
supervised learning. This dynamic annotation procedure fits the active learning perspective …

In traditional active learning, there is only one labeler that always returns the ground truth of
queried labels. However, in many applications, multiple labelers are available to offer …

Document labeling is a critical step in building various machine learning applications.
However, the step can be time-consuming and arduous, requiring a significant amount of …

Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios
where collected training data can contain incorrect or corrupted labels. Most existing …