Training accurate classifiers requires many labels, but each label provides only limited
information (one bit for binary classification). In this work, we propose BabbleLabble, a …

We propose a new framework for supervised machine learning. Our goal is to learn from
smaller amounts of supervised training data, by collecting a richer kind of training data …

The predictions of text classifiers are often driven by spurious correlations--eg, the
termSpielberg'correlates with positively reviewed movies, even though the term itself does …

We extend the traditional active learning framework to include feedback on features in
addition to labeling instances, and we execute a careful study of the effects of feature …

Traditional active learning allows a (machine) learner to query the (human) teacher for
labels on examples it finds confusing. The teacher then provides a label for only that …

Machine learning requires an effective combination of data, features, and algorithms. While
many tools exist for working with machine learning data and algorithms, support for thinking …

Traditional clustering algorithms are inapplicable to many real-world problems where limited
knowledge from domain experts is available. Incorporating the domain knowledge can …

Sentiment analysis is crucial for studying public opinion since it can provide us with valuable
information. Existing sentiment analysis methods rely on finding the sentiment element from …

Active learning is an area of machine learning examining strategies for allocation of finite
resources, particularly human labeling efforts and to an extent feature extraction, in …

Tools for Interactive Machine Learning (IML) enable end users to update models in a “rapid,
focused, and incremental”—yet local—manner. In this work, we study the question of local …