announcement of mlrc 2025

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     url: /
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   - name: Submission Form
-    url: https://forms.gle/JJ28rLwBSxMriyE89
+    url: https://forms.gle/REgwJQBP8ZXQEaJk7
     weight: 20
   - name: Past Iterations
     identifier: past

content/_index.md

@@ -1,39 +1,100 @@
 ---
-title: ML Reproducibility Challenge
 type: book # Do not modify.
 toc: false
 headless: true
 ---
 
-Welcome to the home of ML Reproducibility Challenge. This is an annual event for
-providing a space for research into reproducibility of Machine Learning
-literature.
+<div class="container banner">
+    <div class="row article-banner">
+        <div class="col-md-12 text-center">
+        <h2 class="text-white"> ML Reproducibility Challenge <br>Princeton University <br>New Jersey, USA </h2>
+        <h2 class="text-white">August 21, 2025</h2>
+    </div>
+    </div>
+</div>
+
+## MLRC 2025
+
+Welcome to the home of ML Reproducibility Challenge. This is an annual event
+promoting research into reproducibility of Machine Learning literature.
 ([v1](https://www.cs.mcgill.ca/~jpineau/ICLR2018-ReproducibilityChallenge.html),
 [v2](https://www.cs.mcgill.ca/~jpineau/ICLR2019-ReproducibilityChallenge.html),
 [v3](https://reproducibility-challenge.github.io/neurips2019/),
 [v4](https://paperswithcode.com/rc2020),
 [v5](https://paperswithcode.com/rc2021),
-[v6](https://paperswithcode.com/rc2022), [v7](/proceedings/mlrc2023/)). The
-primary goal of this event is to encourage the publishing and sharing of
-scientific results that are reliable and reproducible. In support of this, the
-objective of this challenge is to investigate reproducibility of papers accepted
-for publication at top conferences by inviting members of the community at large
-to select a paper, and verify the empirical results and claims in the paper by
-reproducing the computational experiments, either via a new implementation or
-using code/data or other information provided by the authors.
+[v6](https://paperswithcode.com/rc2022), [v7](/proceedings/mlrc2023/)). This
+conference is an unique venue in the Machine Learning community to share,
+disseminate and discuss reproducible methods and tools, investigate
+reproducibility of papers accepted for publication at top conferences, and test
+generalizability of scientific findings by adding novel insights and empirical
+results.
 
 {{% callout note %}}
 
+- :bell: MLRC 2025 [Call for Papers](/call_for_papers) is out! Checkout our
+  [announcement](/blog/announcing_mlrc2025) blog post.
 - :mortar_board: [MLRC 2023](/proceedings/mlrc2023/) papers featured in
   [NeurIPS 2024 Poster Sessions](https://neurips.cc/), Dec 10-15, 2024 at
   Vancouver, Canada. If you are attending NeurIPS, do
   [drop by to the posters](/proceedings/) to say hi!
-- Next iteration of MLRC will be **MLRC2025**, and it will be **in-person** - a
-  one-day conference! Announcement will be made very soon, stay tuned!
 
 {{% /callout %}}
 
-{{< tweet user="hugo_larochelle" id="1819465878641262862" >}}
+## Venue
+
+MLRC 2025 will be held _in-person_ as a one-day conference, at Princeton
+University, NJ, USA on August 21st, 2025. The conference will be single-track,
+with a mix of invited talks, oral presentations and poster sessions. Checkout
+our [announcement blog](/blog/announcing_mlrc2025/) for more details!
+
+## Important Dates
+
+- Submit to TMLR OpenReview: https://openreview.net/group?id=TMLR
+- Deadline to share your intent to submit a TMLR paper to MLRC: **February 21st,
+  2025** at the following form: https://forms.gle/REgwJQBP8ZXQEaJk7
+- 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.
+- Cutoff deadline for TMLR decisions: **June 20th, 2025**
+- Deadline for announcing accepted papers: **June 27th, 2025**
+- Conference day: **August 21st, 2025** at Princeton University, NJ, USA
+
+## Organizers
+
+#### General Chair
+
+- [Koustuv Sinha](https://koustuvsinha.com), Meta
+
+#### Program Chairs
+
+- [Jessica Forde](https://jzf2101.github.io/), Brown University
+- [Adina Williams](https://ai.meta.com/people/1396973444287406/adina-williams/),
+  Meta
+- [Angela Fan](https://ai.meta.com/people/423869000175606/angela-fan/), Meta
+- [Mike Rabbat](https://ai.meta.com/people/1148536089838617/michael-rabbat/),
+  Meta
+- [Naila Murray](https://scholar.google.fr/citations?user=suSmYHoAAAAJ&hl=en),
+  Meta
+
+#### Local Chairs
+
+- [Arvind Narayanan](https://www.cs.princeton.edu/~arvindn/), Princeton
+  University, Senior Local Chair
+- [Peter Henderson](https://www.peterhenderson.co/), Princeton University, Local
+  Chair
+- Remi Moss, Executive Director, [Princeton AI Lab](https://ai.princeton.edu/)
+- Ellen DiPippo, Program Manager, [Princeton AI Lab](https://ai.princeton.edu/)
+
+#### Senior Program Chair
+
+- [Joelle Pineau](https://www.cs.mcgill.ca/~jpineau/), Meta / Mila - Quebec AI /
+  McGill University
+
+## Contact
 
-<a href="https://twitter.com/x?ref_src=twsrc%5Etfw" class="twitter-follow-button" data-show-count="false">Follow
-@x</a><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
+- For queries related to the conference, please contact us at
+  [[email protected]](mailto:[email protected]) or
+  [[email protected]](mailto:[email protected])
+- Follow us on Social media for updates: Twitter
+  ([@repro_challenge](https://x.com/repro_challenge)), BlueSky
+  ([@reproml.org](https://bsky.app/profile/reproml.org))

content/blog/announcing_mlrc2023/index.md

@@ -231,9 +231,9 @@ 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.
 
-{{< figure src="../../uploads/mlrc.drawio.svg" class="mlrc_dark" >}}
+{{< figure src="../../uploads/mlrc2023.drawio.svg" class="mlrc_dark" >}}
 
-{{< figure src="../../uploads/mlrc.light.drawio.svg" class="mlrc_light" >}}
+{{< figure src="../../uploads/mlrc2023.light.drawio.svg" class="mlrc_light" >}}
 
 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

content/blog/announcing_mlrc2025/index.md

@@ -0,0 +1,127 @@
+---
+title: Announcing MLRC 2025, our first in-person conference
+toc: true
+type: book
+date: "2024-12-12T00:00:00+01:00"
+draft: false
+hidden: true
+
+# Prev/next pager order (if `docs_section_pager` enabled in `params.toml`)
+weight: 1
+---
+
+We are excited to announce the 8th iteration of the Machine Learning
+Reproducibility Challenge, MLRC 2025, which will also be the first, in-person
+conference, hosted at Princeton University, New Jersey, USA on August 21st,
+2025!
+
+The Machine Learning Reproducibility Challenge (MLRC) is an annual conference
+for reproducibility research in the Machine Learning community. MLRC has been
+running as an online conference for the last seven years
+([v1](https://www.cs.mcgill.ca/~jpineau/ICLR2018-ReproducibilityChallenge.html),
+[v2](https://www.cs.mcgill.ca/~jpineau/ICLR2019-ReproducibilityChallenge.html),
+[v3](https://reproducibility-challenge.github.io/neurips2019/),
+[v4](https://reproducibility-challenge.github.io/neurips2019/),
+[v5](https://paperswithcode.com/rc2021),
+[v6](https://paperswithcode.com/rc2022), [v7](/proceedings/mlrc2023/)). This
+limits the incentives to submit to the conference, as online mode doesn’t offer
+the authors to showcase their work and network among researchers in the same
+domain. We have been systematically trying to address this issue by
+[improving the submission and publication process](/blog/announcing_mlrc2023/),
+and partnering with several conferences over the years, either by a workshop, or
+more recently through a
+[Journal-to-Conference](https://blog.neurips.cc/2022/08/15/journal-showcase/)
+mode with NeurIPS for the last couple of iterations.
+
+The success of the MLRC poster sessions at these conferences, and the recent
+success of [COLM](https://colmweb.org/index.html), motivated us to “graduate”
+MLRC into an in-person conference, starting this iteration. MLRC 2025 will be a
+one-day single track conference, with a mix of invited talks, oral
+presentations, and poster sessions. We hope the conference will provide the much
+needed avenue for discussing and disseminating reproducibility research and
+allow participants and attendees to network over a common goal of improving the
+science of Machine Learning through reproducible methods. We are excited to
+partner with Princeton University, specifically the
+[Princeton AI Lab](https://ai.princeton.edu/ai-lab) for providing us the venue,
+and to [Meta](https://ai.meta.com/research/) for providing us the funds to
+conduct such in-person conference.
+
+As for the nomenclature of the conference, historically we have had one year
+backdated, as in MLRC 2023 actually happens in 2024, due to incorporating papers
+published in 2023. As we move on to be an in-person conference, to closer align
+with the format of ML conferences and also in favor of broadening our scope, we
+are therefore dropping the version 2024 and moving directly to MLRC 2025.
+
+We therefore announce the [call for papers](/call_for_papers/) for MLRC 2025. We
+invite submissions which conduct novel, unpublished research of reproducibility
+of machine learning methods and literature, including but not limited to :
+
+- Methods and tools to foster reproducibility research in Machine Learning
+- Generalisability of published claims: novel insights and results beyond what
+  was presented in the original paper, from any paper (or set of papers)
+  published in top ML conferences and journals.
+- Meta-reproducibility studies on a set of related papers.
+- Meta analysis on the state of reproducibility in various subfields in Machine
+  Learning.
+
+Submissions must be first accepted at [TMLR](https://jmlr.org/tmlr/) to be
+considered in the MLRC 2025 Proceedings. Please read the
+[author guidelines](https://jmlr.org/tmlr/author-guide.html) and
+[submission guidelines](https://jmlr.org/tmlr/editorial-policies.html) from TMLR
+to get the submission format and to understand more about the reviewing process.
+Existing papers related to the scope (with reproducibility certification)
+already published at TMLR are also welcome for the consideration of the
+committee.
+
+{{< figure src="../../uploads/mlrc2025.drawio.svg" class="mlrc_dark" >}}
+
+{{< figure src="../../uploads/mlrc2025.light.drawio.svg" class="mlrc_light" >}}
+
+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 21st, 2025. We aim to announce the accepted papers by June 27th. We
+have set a cutoff deadline for accepting TMLR decisions one week prior to the
+announcement deadline, allowing ample time for you to ensure your paper has
+received the decision at TMLR, and update our forms accordingly. For logistical
+purposes, this date will be a hard deadline, and unfortunately we would not be
+able to accommodate any late decisions from TMLR post this date. Therefore, we
+encourage you to submit early to TMLR, and contact the TMLR Action Editors well
+in advance if your paper hasn’t been reviewed or is pending decisions. If you
+miss the cutoff deadline, we encourage you to still go through the TMLR review
+cycle, as then your paper once published will be eligible for the next year's
+iteration (MLRC 2026). If you already have a relevant published TMLR paper which
+has not been showcased at MLRC 2023, you can directly submit it now to our
+system for consideration for MLRC 2025.
+
+## Important dates
+
+- Submit to TMLR OpenReview: https://openreview.net/group?id=TMLR
+- Deadline to share your intent to submit a TMLR paper to MLRC: **February 21st,
+  2025** at the following form: https://forms.gle/REgwJQBP8ZXQEaJk7
+- 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.
+- Cutoff deadline for TMLR decisions: **June 20th, 2025**
+- Deadline for announcing accepted papers: **June 27th, 2025**
+- Conference day: **August 21st, 2025** at Princeton University, NJ, USA
+
+In the following months, we will share more updates about the conference
+session, invited talks, program and registration. We are excited that this will
+be a first, in-person conference specifically focused on reproducibility in
+machine learning research, which will foster the research and discussion on
+reproducible methods, analysis, insights and further strengthen and promote the
+scientific understanding of Machine Learning.
+
+We are looking for co-organizers and volunteers! If you wish to help us in
+organizing this in-person conference, or would like to nominate organizers /
+volunteers, please
+[submit the following form](https://forms.gle/w8MtswWEbBWQVZbEA). You can also
+contact us at [[email protected]](mailto:[email protected]) or
+[email protected] if you have any feedback / suggestions.
+
+Looking forward to a successful conference next year!
+
+Koustuv Sinha, General Chair
+
+_on behalf of the MLRC 2025 Organizers_

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@@ -1,20 +1,16 @@
 ---
 # Title, summary, and page position.
-linktitle: Organizers
-weight: 100
+linktitle: Advisory Board 
+weight: 700
 icon: task-square-svgrepo-com
 icon_pack: fas
 
 # Page metadata.
-title: Organizers
-date: "2023-10-22T00:00:00Z"
+title: Advisory Board 
+date: "2024-12-12T00:00:00Z"
 type: book # Do not modify.
 ---
 
-## Program Chair
-
-- [Koustuv Sinha](https://koustuvsinha.com/), _Meta (FAIR)_
-
 ## Reproducible ML Advisory Board
 
 - [Joelle Pineau](https://www.cs.mcgill.ca/~jpineau/), _Meta (FAIR), McGill
@@ -28,11 +24,3 @@ type: book # Do not modify.
 - [Arvind Narayanan](https://www.cs.princeton.edu/~arvindn/), _Princeton
   University_
 - [Jesse Dodge](https://jessedodge.github.io/), _Allen Institute for AI_
-
-<!-- ## Acknowledgements -->
-<!---->
-<!-- - Reviewers -->
-<!-- - Organizers -->
-<!-- - PapersWithCode -->
-<!-- - Kaggle -->
-<!-- - OpenReview -->