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 …

Recent studies have indicated that foundation models, such as BERT and GPT, excel at
adapting to various downstream tasks. This adaptability has made them a dominant force in …

Successful machine learning (ML) applications require iterations on both modeling and the
underlying data. While prior visualization tools for ML primarily focus on modeling, our …

One major cause of performance degradation in predictive models is that the test samples
are not well covered by the training data. Such not well-represented samples are called OoD …

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 …

Crowdsourcing, a distributed human problem-solving paradigm is an active research area
which has attracted significant attention in the fields of computer science, business, and …

Labeled datasets are essential for supervised machine learning. Various data labeling tools
have been built to collect labels in different usage scenarios. However, developing labeling …

In this paper, we develop a visual analysis method for interactively improving the quality of
labeled data, which is essential to the success of supervised and semi-supervised learning …

Data-centric AI has emerged as a new research area to systematically engineer the data to
land AI models for real-world applications. As a core method for data-centric AI, data …

Accurate and timely estimation of incurred damages is a critical component of effective
disaster management, usually performed by trained inspectors and experts. The limitations …