Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Interactive machine learning (IML) is an iterative learning process that tightly couples a
human with a machine learner, which is widely used by researchers and practitioners to …

There is fast‐growing literature on provenance‐related research, covering aspects such as
its theoretical framework, use cases, and techniques for capturing, visualizing, and …

Visual analytic tools combine the complementary strengths of humans and machines in
human-in-the-loop systems. Humans provide invaluable domain expertise and …

It has been widely suggested that a key goal of visualization systems is to assist decision
making, but is this true? We conduct a critical investigation on whether the activity of …

The assignment of labels to data instances is a fundamental prerequisite for many machine
learning tasks. Moreover, labeling is a frequently applied process in visual interactive …

In the face of complex decisions, people often engage in a three-stage process that spans
from (1) exploring and analyzing pertinent information (intelligence);(2) generating and …

Various domain users are increasingly leveraging real-time social media data to gain rapid
situational awareness. However, due to the high noise in the deluge of data, effectively …

In order to effectively infer correct labels from noisy crowdsourced annotations, learning-from-
crowds models have introduced expert validation. However, little research has been done …

Exploratory data analysis plays a major role in obtaining insights from data. Over the last two
decades, researchers have proposed several visual data exploration tools that can assist …