SourceReddit r/MachineLearning•Stalecollected in 71m
ICML Anonymized Git Repos for Rebuttals OK?
#conference#rebuttal#anonymityicmlicmlanonymous-4open-science
💡Clarifies ICML policy on anon git repos—key for rebuttal submissions in ML conferences.
⚡ 30-Second TL;DR
What Changed
Papers submit extra figures/code via anonymized repos for ICML rebuttals
Why It Matters
Poster seeks policy confirmation before submitting graphs.
What To Do Next
Check ICML 2024 reviewer guidelines on supplementary materials before using anonymized repos.
Who should care:Researchers & Academics
Key Points
- •Papers submit extra figures/code via anonymized repos for ICML rebuttals
- •Common platform: anonymous.4open.science
- •Reviewer asks if this violates anonymity policies
- •Considering similar for discussion phase graphs
🧠 Deep Insight
AI-generated analysis for this event — not the original article.
🔑 Enhanced Key Takeaways
- •ICML's double-blind review policy explicitly mandates that authors must not reveal their identity, and while anonymous repositories are a common workaround, they are frequently scrutinized by Area Chairs for potential 'de-anonymization' risks if metadata or specific coding styles are identifiable.
- •The use of platforms like anonymous.4open.science is not officially endorsed by ICML; rather, it is a community-adopted practice that exists in a regulatory gray area, often tolerated provided the repository does not contain identifiable information such as author names, affiliations, or links to personal GitHub profiles.
- •Recent conference guidelines have increasingly emphasized that supplementary material provided during the rebuttal phase must adhere to the same strict anonymity standards as the original submission, leading to a trend where conferences are moving toward centralized, conference-managed hosting solutions to mitigate security risks.
🔮 Future ImplicationsAI analysis grounded in cited sources
ICML will transition to a centralized, conference-controlled repository system for all rebuttal materials.
The increasing security risks and ambiguity surrounding third-party anonymous hosting services are forcing major AI conferences to standardize infrastructure to ensure compliance with double-blind policies.
Automated de-anonymization detection tools will be integrated into the ICML submission portal.
As reliance on external repositories grows, conferences will likely deploy automated scanners to detect metadata, file paths, or coding patterns that could inadvertently reveal author identities.
⏳ Timeline
2018-05
Introduction of anonymous.4open.science to support double-blind peer review in scientific publishing.
2021-02
ICML updates submission guidelines to explicitly address the handling of supplementary material and anonymity requirements.
2024-06
ICML organizers issue clarifications regarding the use of external links during the rebuttal phase to prevent accidental de-anonymization.
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Original source: Reddit r/MachineLearning ↗
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