Announcing MLRC 2023
Every year since 2018, we have conducted the Machine Learning Reproducibility Challenge (see previous years v1, v2, v3, v4, v5, v6), which invites the ML community to examine the reproducibility of existing, recently published papers in top conferences. As we gear up to announce the seventh iteration of the challenge, MLRC 2023, we would like to share our learnings from previous years and how we plan to incorporate these lessons into the upcoming challenge.
Over the years, the ML Reproducibility Challenge has been largely aimed at students beginning their journey in ML research as part of their ML coursework. This provided an accessible entry point to ML research, allowing early career researchers to participate and learn a full paper publication lifecycle - from designing the research question to investigating the limits of a scientific hypothesis to final publication acceptance. One of the success stories of this approach was from the University of Amsterdam, which designed a course around the challenge (FACT AI), and consistently produced high quality reproducibility reports in the final proceedings of the challenge. While the challenge is a valuable resource to ML course instructors and early career ML researchers, we are eager for the challenge to grow in scope and impact. More specifically, we want to encourage ML researchers to contribute novel research that will improve scientific practice and understanding in the field. We thus identified several shortcomings of the current model following our retrospection of the submitted and accepted papers in the challenge.
While the challenge is a valuable resource to ML course instructors and early career ML researchers, we are eager for the challenge to grow in scope and impact. More specifically, we want to encourage ML researchers to contribute novel research that will improve scientific practice and understanding in the field. We thus identified several shortcomings of the current model following our retrospection of the submitted and accepted papers in the challenge.
Reproducibility is not a binary outcome
The term “reproducibility” unfortunately comes with a baggage - whenever we talk about a paper to be reproducible, the expectation is this binary property - yes or no. However, the reality is way more nuanced: a paper presents multiple hypotheses (claims) of varying importance to the central claim - which some of them can be directly reproducible, others might not be; some of the claims may even be limited in terms “generalisability”. Consequently, we consistently found the quality of the reports submitted to the challenge fall into either of these two categories: a) making a sweeping claim about reproducibility, or b) diving deep and constructing a holistic view of reproducibility, replicability and generalisability of the claims presented in the original paper. Not surprisingly, the latter cohort is always highly rated by the reviewers and ends up more often in the accepted pool. From the 2020 iteration, we introduced a Reproducibility Summary template to encourage participants to focus on the central claims of the paper, and to mainly focus on this generalisability aspect - results beyond the original paper. We found that introducing this template helps the authors to focus more on these questions, thereby improving their submission.
Reproducibility is not about whether author’s code gives the same results
Thanks to the continued effort made by the ML community in terms of Checklists and mandatory code submission policies, we now see >90% of papers accompanied by their source code. This is a very promising progress regarding reproducibility of the research in our field - the presence of code alleviates many questions and issues regarding the implementation, thereby facilitating exact reproducibility. Inadvertently, this also resulted in many MLRC submissions where authors only run the provided code and compare the numbers. While these contributions measure replicability, they are not strong research contributions which add valuable insights to the field. Instead, strong submissions tend to leverage the authors code to make exhaustive ablations, hyperparameter search and explore generalisability results on different data/models.
Redundant reproductions of the same resource-friendly papers
For several years, we find that authors tend to pick papers which are more resource-friendly - i.e. papers which can run on a single commodity GPU. This is likely a side-effect of the challenge being targeted primarily towards early career researchers. While reproducibility study on such resource-light papers is not a problem per se, it does often result in multiple reproduction reports on the same paper. We hypothesize that this is probably due to courses assigning multiple groups to work on a single paper, in order to better manage logistics. As we did not have any deduplication criteria, we explicitly inform our reviewers to not penalize multiple reproducibility reports on the same paper. We aimed to reduce this by introducing a pre-registration phase early on (2019, 2020), however that turned out to be logistically challenging leading us to discontinue it. In our opinion, cherry-picking the same paper reduces the breadth of papers being reproduced in the challenge, invites duplication in work and overall lessens the scientific contribution to the community.
Low signal reviews due to inexperienced reviewers
Reviewing for the ML Reproducibility Challenge is unique - it requires the reviewer to first read and understand the original paper(s) and then perform a critical judgment of the reproducibility report. Hence, workload wise, reviewing for this challenge requires twice the amount of time per paper than a standard ML conference. We therefore typically try to evenly reduce the reviewing workload, with a maximum of two papers per reviewer. Over the last several iterations, barring from the top reviewers, we observed a concerning trend of low signal reviews. We hypothesize this mainly due to the different format and higher workload. To remedy this, we have introduced comprehensive reviewer guidelines, and also awarded top reviewer awards to further incentivize high quality reviews. We are grateful to our reviewers for their consistent support, and we have observed a steady number of reviewers who consistently provide high quality, useful reviews and hence feature in the top reviewers list on multiple occasions.
Low incentives to publish a reproducibility report
From the inception of the challenge, we have partnered with ReScience as our journal publication medium. ReScience is a peer reviewed, open journal focusing on reproducibility reports across many different fields of computational science, making it a unique venue. ReScience journal editorial process is open and live on Github, making it very convenient to access. However, we have observed that the popularity of ReScience in the Machine Learning community is still low, limiting the incentives of publication at the challenge. Furthermore, we found ReScience journal entries are not yet properly indexed by Google Scholar, although the editors are working hard to fix that. Another issue was since MLRC is not a workshop at any major conference, the original format did not have any option to present papers to the community, hurting the incentives even further. Since 2022, we have partnered with NeurIPS to allow poster presentations of accepted papers at the Journal to Conference Track, which significantly increases the incentive and prestige of publishing papers at MLRC. We have also partnered with Kaggle in our last iteration to provide accepted papers compute credits to further incentivize submission and high quality research. Authors of top papers were granted a significant amount of compute credits by Kaggle to further pursue their research.
On the road ahead
We want to continue improving the challenge on the following aspects: broadening the target audience, broadening the scope and improving incentives, to make the challenge more exciting to the community and encourage reproducible research.
We are thus happy to announce the formal partnership with Transactions of Machine Learning Research (TMLR) journal. TMLR is a new journal in the ML community, which is under the umbrella of Journal of Machine Learning Research (JMLR), and has been fast growing in significance and reputation within the field. Unlike JMLR, TMLR caters to shorter format manuscripts similar to conference proceedings, and employs a fast and open reviewing cycle, ensuring high quality submissions. Therefore, in the upcoming iteration (MLRC 2023), papers will be published at TMLR instead of ReScience.
Broadening the target audience
While the MLRC will still be useful for the early-career researchers in ML courses, we want to expand and encourage submissions from the broader community, including academia and industry. Since TMLR publication accounts for significantly high prestige and reception in the ML community, we hope this change would attract a broad range of researchers to contribute to the advancement of our understanding of reproducibility.
Increasing the bar of submissions
As we look forward, the focus of a reproducibility paper should be much more than mere reproduction - it should ideally investigate the generalisability of the original claims. Results and investigations beyond what the authors proposed are therefore encouraged, which adds to the novelty of the contribution. We discourage simple reproduction work - while they are useful, they do not provide enough value to the community. Submissions having multi-paper, topic-based focused contributions are preferred over single paper reproductions. Novel work on tools to investigate and enable reproducible research are also welcome to the submission. We also recommend you to read TMLR’s submission guidelines and editorial policies which also applies equally to MLRC submissions.
Implementing a comprehensive and open reviewing cycle
As we partner with TMLR, we also leverage their open, comprehensive reviewing mechanism. Papers submitted to MLRC would first undergo TMLR’s reviewing process. TMLR employs rich and diverse reviewers from the ML community, along with expert Action Editors. Reviews will be viewed publicly on OpenReview, and TMLR comes with a quick reviewing turnaround which includes author rebuttals - a highly requested feature in our previous iterations.
Improving incentives to participate in the challenge
Publication of MLRC papers at TMLR will improve the reception and dissemination of the work in the broader ML community. Accepted papers at TMLR are announced in mailing lists and social media on a regular basis. Papers accepted at TMLR are indexed in Google Scholar using the existing OpenReview mechanism, allowing easy citations and tracking cited counts. We also hope to continue our existing partnership with NeurIPS to present accepted papers in the Journal to Conference Showcase Track, allowing further dissemination and opportunity to gain feedback from the ML community. (If you are attending NeurIPS 2023 in person, checkout the Journal to Conference Track poster session for MLRC 2022 accepted papers!)
Providing a new home for MLRC web
We are happy to announce our new and permanent online home, reproml.org. Announcements, information and blog posts about MLRC 2023 and all subsequent iterations will be hosted in this dedicated space. We are grateful to PapersWithCode for providing online hosting for our past three iterations!
MLRC 2023 Call for Papers
Finally, we are happy to formally announce MLRC 2023, which will go live starting on October 23rd! We invite contributions from academics, practitioners and industry researchers of the ML community to submit novel and insightful reproducibility studies. Please read our Call for Papers for more information.
We recommend you choose any paper(s) published in the 2023 calendar year from the top conferences and journals (NeurIPS, ICML, ICLR, ACL, EMNLP, ECCV, CVPR, TMLR, JMLR, TACL) to run your reproducibility study on.
In order for your paper to be submitted and presented at MLRC 2023, it first needs to be accepted and published at TMLR. While TMLR aims to follow a 2-months timeline to complete the review process of its regular submissions, this timeline is not guaranteed. If you haven’t already, we therefore recommend submitting your original paper to TMLR by February 16th, 2024, that is a little over 3 months in advance of the MLRC publication announcement date.
- Challenge goes live: October 23, 2023
- Deadline to share your intent to submit a TMLR paper to MLRC: February 16th, 2024 Form: https://forms.gle/JJ28rLwBSxMriyE89. This form requires that you provide a link to your TMLR submission. Once it gets accepted (if it isn’t already), you should then update the same form with your paper camera ready details.
- Your accepted TMLR paper will finally undergo a light AC review to verify MLRC compatibility.
- We aim to announce the accepted papers by May 31st, 2024, pending decisions of all papers.
As we begin a new era of reproducibility research in Machine Learning, we hope our continued quest for high quality reproducibility studies will inspire the community to not only investigate the claims of existing papers, but add novel research insights and contributions to the literature, accelerating the progress of science. We hope these steps towards improving the incentives of investing in reproducibility research enables the community to produce higher quality scientific contributions.
The MLRC 2023 Organizing Team thanks Joelle Pineau, Hugo Larochelle and Gautam Kamath on feedback for drafting this blog post.