  We propose AutoLegend to generate interactive visualization legends using
online learning with user feedback. AutoLegend accurately extracts symbols and
channels from visualizations and then generates quality legends. AutoLegend
enables a two-way interaction between legends and interactions, including
highlighting, filtering, data retrieval, and retargeting. After analyzing
visualization legends from IEEE VIS papers over the past 20 years, we
summarized the design space and evaluation metrics for legend design in
visualizations, particularly charts. The generation process consists of three
interrelated components: a legend search agent, a feedback model, and an
adversarial loss model. The search agent determines suitable legend solutions
by exploring the design space and receives guidance from the feedback model
through scalar scores. The feedback model is continuously updated by the
adversarial loss model based on user input. The user study revealed that
AutoLegend can learn users' preferences through legend editing.
