As the performance and complexity of machine learning models have grown significantly
over the last years, there has been an increasing need to develop methodologies to …

To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling
(HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches …

In 2021, the US Hispanic population totaled 62.5 million people, 68% of whom spoke
Spanish in their homes. To date, it is unclear which political advertisers address this …

Topic modeling is a text analysis technique for automatically discovering common themes in
a collection of documents.“Human-in-the-loop” topic modeling (HLTM) allows domain …

Transparency and explainability in image classification are essential for establishing trust in
machine learning models and detecting biases and errors. State-of-the-art explainability …

We explore predictability and control in interactive systems where controls are easy to
validate. Human-in-the-loop techniques allow users to guide unsupervised algorithms by …

This article presents a new Human-steerable Topic Modeling (HSTM) technique. Unlike
existing techniques commonly relying on matrix decomposition-based topic models, we …

Human-in-the-loop topic models allow users to encode feedback to modify topic models
without changing the core machinery of the topic models. Basic refinement functions have …

Human-in-the-loop topic modeling (HLTM) empowers users to modify topic models and
enhance their quality, by enabling them to directly refine the model. Prior studies have made …

This paper presents a Steerable Neural Topic Modeling (SNTM) technique. Unlike existing
techniques commonly relying on statistic-based topic models, we employ an autoencoder …