Reproducibility in

Machine Learning

An ICLR 2019 Workshop


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 are the driving force of innovation and differentiation in the industry, so quick and accurate implementation is really critical. On the research side they can help us 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 run.
  • 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?
  • A lot of people tend to understand an algorithm by looking at code and not by following equations. How can we come up with a framework of publishing that includes them. Is pseudocode the best we can do?
  • While scientific papers often do an importance analysis of the features, ML papers rarely do proper attribution on the importance of algorithmic components and hyperparameters. What is the best way to “unit-test” an algorithm and do attribution of the results to certain components and hyperparameters
  • 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?
  • Part of ensuring reproducibility of state-of-the-art is ensuring fair comparisons, proper experimental procedures, and proper evaluation methods and metrics. To this end, what are the proper guidelines for such aspects of machine learning problems? How do they differ among subsets of machine learning?

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. Some examples of this include experimental-driven investigations as in [1,2,3]
  • Investigations and proposals of proper experimental procedure and evaluation methodologies which ensure reproducible and fair comparisons in novel literature [4]
  • Evidence-driven works investigating the importance of reproducibility in machine learning and science in general
  • Connections between the reproducibility situation in Machine Learning and other fields
  • Rigorous replications, both failed and successful, of influential papers in the Machine Learning literature.

We will accept both short paper (4 pages) and long paper (8 pages) submissions (not including references). Submissions should be in the NIPS 2018 format. A few papers may be selected as oral presentations, and the other accepted papers will be presented in a poster session. There will be no proceedings for this workshop, however, upon the author’s request, accepted contributions will be made available in the workshop website. Submission are single-blind, peer-reviewed on OpenReview (, and open to already published work.

Important Dates

Workshop Paper Submission Deadline: 5 March 2019 11:59pm EST

Workshop Paper Decision: TBD

Camera Ready Deadline: TBD

Workshop Date: May 6, 2019.

Workshop Schedule

May 6, 2019 ICLR, New Orleans

9:50-9:55, Opening Remarks

9:55-10:30 Invited Talk: Joel Grus, Title: Reproducibility as a Vehicle for Engineering Best Practices

10:30-11:00 Coffee / Poster Session.

11:00-11:30 Invited Talk: Pablo Samuel Castro, Title: Dopamine: thoughts on reproducibility

11:30-12:00 Contributed Talk: Francesco Locatello, Title: Challenging common assumptions in the unsupervised learning of disentangled representations

12:00-12:30 Contributed Talk: Luca Foschini, Title: Reproducibility in Machine Learning for Health

12:30-13:00 Contributed Talk: Brian Lee, Title: Minigo: A Case Study in Reproducing Reinforcement Learning Research

13:00-15:20 Lunch and Conference Keynote

15:20-15:40 Contributed Talk: Arnout Devos, Title: Reproducing Meta-learning with differentiable closed-form solvers

15:40-16:00 Invited Talk: Avital Oliver, Title: How can reproducibility support understanding?

16:00-16:15 Coffee / Poster Session.

16:15-16:30 Invited Talk: Soumith Chintala, Title: A checklist for open-sourcing your code for reproducibility and extensibility

16:30-17:00 Invited Talk: Jessica Zosa Forde, Title: Promoting Science in Machine Learning Research

17:00-17:30 Invited Talk: Dhruv Madeka, Title: On reproducible research for large scale production deployments

17:30-18:30 Panel Discussion with Hugo Larochelle, Nando De Freitas, Joel Grus, Jessica Zosa Forde, Pablo Samuel Castro

Invited Speakers and Panelists

More to be added soon!

Joel Grus

Title: Reproducibility as a Vehicle for Engineering Best Practices


As an engineer, I'm a strong advocate for software engineering best practices; and as someone who works in AI, I'm a strong advocate for reproducible research. Fortunately for me, these advocacies overlap, as it turns out that good engineering practices make reproducibility much easier.

In this talk I'll discuss the various reasons reproducibility is important, demonstrate how good engineering discipline leads to more reproducible science, and give some examples from my own work.

Pablo Samuel Castro

Title: Dopamine: thoughts on reproducibility


Last summer we released Dopamine, a library for flexible reinforcement learning research, which has been well received by the community. We built the design of the framework aiming for simplicity, legibility, and flexibility, to enable fast prototyping of new ideas. We believe these design choices are largely responsible for the reception our framework has received. In this talk I will reflect on some of these design choices and how they can help address some of the reproducibility issues that have recently been raised. I will frame these discussions within the context of my academic career, which includes an extended hiatus at the time when the deep learning revolution began!

Avital Oliver

Title: How can reproducibility support understanding?


Let's talk about research code. In this talk, I will compare the constraints in which those are developed with a different way to reproduce a paper -- namely Colab notebooks that can be easily run, explored and shared. These notebooks allow for rich structure unique fit for reproducing papers in a way that facilitates /understanding/. I will propose looking at Depth First Learning, a new ML pedagogical system as a hub for Colab notebooks building up in steps towards important ML papers.

Dhruv Madeka

Title: On reproducible research for large scale production deployments

Soumith Chintala

Title: A checklist for open-sourcing your code for reproducibility and extensibility

Abstract: In this talk, we shall aim to develop a checklist that helps you release your paper, code and data in a way that a maximal number of people can use it. We shall discuss constraints and perspectives of people that you as the author often miss, and to think and improve your release keeping these perspectives in mind.

Jessica Forde

Title: Promoting Science in Machine Learning Research

Abstract: Some researchers have called for increased scientific scrutiny of developments in the field of machine learning, emphasizing the need for advancements in theory, reproducibility, and generalization. Others have observed that successful engineering projects have inspired theoretical understanding. This talk examines how the research community can improve their engineering practices to drive further advances in the field. It demonstrates how we can make full use of open source to build and share experiments to promote mutual understanding and facilitate further research. It examines how we can borrow best practices from other fields of science (psychology, clinical medicine,high energy physics) to further enhance research practices in the community. Work produced in conjunction with researchers at Project Jupyter, FAIR MPK, Harvard, and Mt. Sinai Hospital.


  • Rosemary Nan Ke, (MILA) École Polytechnique de Montréal
  • Alex Lamb, (MILA) Université de Montréal
  • Olexa Bilaniuk, (MILA) Université de Montréal
  • Anirudh Goyal, (MILA) Université de Montréal
  • Yoshua Bengio, (MILA) Université de Montréal



Readings and References

[1] Lucic, Mario, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet. "Are GANs Created Equal? A Large-Scale Study." arXiv preprint arXiv:1711.10337 (2017).

[2] Melis, Gábor, Chris Dyer, and Phil Blunsom. "On the state of the art of evaluation in neural language models." arXiv preprint arXiv:1707.05589 (2017).

[3] Henderson, Peter, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. "Deep reinforcement learning that matters." arXiv preprint arXiv:1709.06560 (2017).

[4] Nie, Xinkun, Xiaoying Tian, Jonathan Taylor, and James Zou. "Why adaptively collected data have negative bias and how to correct for it." (2017).