All of Bayesian Nonparametrics



The BNP@NeurIPS 2018 workshop will highlight recent advances in modeling and computation through the lens of applied, domain-driven problems that require the infinite flexibility and interpretability of Bayesian Nonparametrics (BNP). The workshop will consist of lively poster sessions, contributed and invited talks, and expert panel discussions. Through these activities, we hope to highlight cutting-edge advances in modeling and inference while also addressing the limitations of existing methods and identifying key open problems for future research. A major focus of the workshop will be to expose participants to practical software tools for performing Bayesian nonparametric analyses.

The workshop will take place on Friday December 7, 2018 at NeurIPS 2018.

Palais des Congrès de Montréal, Montréal CANADA - Room 517 D

Contact: (and check the FAQ page for questions)


  • Travel Award Winners:
    • Irineo Cabreros - ISBA@NeurIPS Award
    • Runjing Liu - ISBA@NeurIPS Award
    • Miriam Shiffman - Google Award
    • Leo Duan - BNP@NeurIPS Award
    • Adam Farooq - BNP@NeurIPS Award
    • Lorenzo Masoero - BNP@NeurIPS Award
  • TensorFlow Probability lunch tutorial: installation info for TensorFlow and TensorFlow Probability
  • Submit a question for the panel discussion here.
  • Accepted papers list posted (all accepted papers will have a poster presentation)
  • Information for Presenters:
  • We will close submissions after the Round 1 deadline, and reopen EasyChair after 10/21/18 for Round 2 submissions.
  • Round 1 Deadline Extended to Thursday 10/11/18. We strongly recommend submitting to Round 1 if you need a NIPS workshop registration. We ask that authors indicate in the submission whether the presenting author needs a workshop registration. See Call for Papers for updated instructions.
  • Call for Papers with submission information posted

Previous workshops:

Invited Speakers and Panelists

Invited Speakers: Speaker abstracts and bios available on Schedule page.

  • Hamsa Balakrishnan (MIT) - Modeling the Fuel Consumption of Aircrafts
  • Benjamin Bloem-Reddy (Oxford) - Left-neutrality: an old friend in the mirror
  • Allison Chaney (Princeton) - Nonparametric Deconvolution Models
  • Joseph Futoma (Harvard) - Learning to Detect Sepsis with a Multi-output Gaussian Process RNN Classifier (in the Real World!)
  • Sinead Williamson (UT Austin) - Random clique covers for graphs with local density and global sparsity

Research Panel: Moderated by Erik Sudderth (UC Irvine)

  • Barbara Engelhardt (Princeton)
  • Tom Griffiths (Princeton)
  • Isabel Valera (Max Planck Institute)
  • Hanna Wallach (Microsoft Research)
  • Sinead Williamson (UT Austin)

Software Panel: Moderated by Mike Hughes (Tufts)

  • David Duvenaud (U of Toronto)
  • Matt Hoffman (Google)
  • Ben Letham (Facebook)
  • Dustin Tran (Columbia & Google)
  • Aki Vehtari (Aalto University)

Lunch Session Software Tutorial:

TensorFlow Probability Tutorial - Matt Hoffman (Google)

Lunch will be provided.


Organizing Committee:

Advisory Committee: