Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

The problem of classification has been widely studied in the data mining, machine learning,
database, and information retrieval communities with applications in a number of diverse …

Although interactive learning puts the user into the loop, the learner remains mostly a black
box for the user. Understanding the reasons behind predictions and queries is important …

It is difficult to apply machine learning to new domains because often we lack labeled
problem instances. In this paper, we provide a solution to this problem that leverages …

Visual text analytics has recently emerged as one of the most prominent topics in both
academic research and the commercial world. To provide an overview of the relevant …

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 …

We present ReGroup, a novel end-user interactive machine learning system for hel**
people create custom, on demand groups in online social networks. As a person adds …

Methods that learn from prior information about input features such as generalized
expectation (GE) have been used to train accurate models with very little effort. In this paper …

In many information processing tasks, labels are usually expensive and the unlabeled data
points are abundant. To reduce the cost on collecting labels, it is crucial to predict which …

A human annotator can provide hints to a machine learner by highlighting contextual
“rationales” for each of his or her annotations (Zaidan et al., 2007). How can one exploit this …