Reproducibility in

Machine Learning

Reproducibility in ML Workshop, ICML'17


This workshop focuses on issues of reproducibility and replication of results in the Machine Learning community.

Papers from the Machine Learning community are supposed to be a valuable asset. They can help to inform and inspire future research. They can be a useful educational tool for students. They can give guidance to applied researchers in industry. Perhaps most importantly, they can help us to answer the most fundamental questions about our existence - what does it mean to learn and what does it mean to be human? Reproducibility, while not always possible in science (consider the study of a transient astrological phenomenon like a passing comet), is a powerful criteria for improving the quality of research. A result which is reproducible is more likely to be robust and meaningful and rules out many types of experimenter error (either fraud or accidental).

There are many interesting open questions about how reproducibility issues intersect with the Machine Learning community:

• How can we tell if papers in the Machine Learning community are reproducible even in theory? If a paper is about recommending news sites before a particular election, and the results come from running the system online in production - it will be impossible to reproduce the published results because the state of the world is irreversibly changed from when the experiment was ran.

• What does it mean for a paper to be reproducible in theory but not in practice? For example, if a paper requires tens of thousands of GPUs to reproduce or a large closed-off dataset, then it can only be reproduced in reality by a few large labs.

• For papers which are reproducible both in theory and in practice - how can we ensure that papers published in ICML would actually be able to replicate if such an experiment were attempted?

• What does it mean for a paper to have successful or unsuccessful replications?

• Of the papers with attempted replications completed, how many have been published?

• What can be done to ensure that as many papers which are reproducible in theory fall into the last category?

• On the reproducibility issue, what can the Machine Learning community learn from other fields?

Invited Speakers

  • John Langford, Microsoft Research
  • Hugo Larochelle, Google Brain
  • Oriol Vinyals, Google Deepmind
  • Jason Weston, Facebook AI Research
  • Damjan Vukcevic, University of Melbourne
  • Joaquin Vanschoren, Eindhoven University of Technology
  • Robert Williamson, Australian National University

Call for Papers

Our aim in the following workshop is to raise the profile of these questions in the community and to search for their answers. In doing so we aim for papers focusing on the following topics:

• Analysis of the current state of reproducibility in machine learning venues

• Tools to help increase reproducibility

• Evidence that reproducibility is important for science

• Connections between the reproducibility situation in Machine Learning and other fields

• Replications, both failed and successful, of influential papers in the Machine Learning literature.

Important Dates

We will be accepting extended abstracts of 2-6 pages in length, not including references. Submissions should be in the NIPS 2017 format

The referring will be single blind and performed on openreview ( Accepted papers will be presented at a poster session during the workshop. A few papers may be accepted for oral presentation.

Workshop Deadline: June 22nd

Workshop Decision: July 1st

Camera Ready Deadline: August 1st.

Submission Instructions will be posted soon.

Workshop Schedule

August 11th, 2017. (part of ICML in Sydney Australia).

Specific times TBD, but will run for the entire day.


  • Anirudh Goyal, (MILA) Université de Montréal
  • Rosemary Nan Ke, (MILA) École Polytechnique de Montréal
  • Alex Lamb,(MILA) Université de Montréal
  • Joelle Pineau, Mcgill University
  • Samy Bengio, Google Brain
  • Yoshua Bengio, Université de Montréal