Statistical topic models are a class of Bayesian latent variable models, originally developed for analyzing the semantic content of large document corpora. With the increasing availability of other large, heterogeneous data collections, topic models have been adapted to model data from fields as diverse as computer vision, finance, bioinformatics, cognitive science, music, and the social sciences. While the underlying models are often extremely similar, these communities use topic models in different ways in order to achieve different goals. This one-day workshop will bring together topic modeling researchers from multiple disciplines, providing an opportunity for attendees to meet, present their work and share ideas, as well as inform the wider NIPS community about current research in topic modeling. This workshop will address the following specific goals:
  • Identify and formalize open research areas
  • Propose, explore, and discuss new application areas
  • Discuss how best to facilitate transfer of research ideas between application domains
  • Direct future work and generate new application areas
  • Explore novel modeling approaches and collaborative research directions

Program Committee

  • Edo Airoldi
  • Hal Daumé
  • Tom Dietterich
  • Laura Dietz
  • Jacob Eisenstein
  • Tom Griffiths
  • John Lafferty
  • Li-Jia Li
  • Andrew McCallum
  • David Mimno
  • Dave Newman
  • Padhraic Smyth
  • Erik Sudderth
  • Yee Whye Teh
  • Chong Wang
  • Max Welling
  • Sinead Williamson
  • Frank Wood
  • Jerry Zhu