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 …

The tremendous growth of event dissemination over social networks makes it very
challenging to accurately discover and track exciting events, as well as their evolution and …

Inspired by the great success of machine learning (ML), researchers have applied ML
techniques to visualizations to achieve a better design, development, and evaluation of …

New trends in innovation management may require new research methods. Social media
analytics (SMA)—a method for capturing and analyzing data from user-generated content …

The exponential growth of textual data presents substantial challenges in management and
analysis, notably due to high storage and processing costs. Text classification, a vital aspect …

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 …

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 …

Hierarchical clustering is an important technique to organize big data for exploratory data
analysis. However, existing one-size-fits-all hierarchical clustering methods often fail to meet …