Tackling Vague ICML Rebuttal Feedback
๐กPractical tips for ICML rebuttals on vague reviews โ vital for ML submitters
โก 30-Second TL;DR
What Changed
Reviewer: experiments improved paper but details partially clarified
Why It Matters
Highlights rebuttal challenges in top ML conferences. Advice could help improve acceptance odds for similar cases.
What To Do Next
Draft a targeted response anticipating common unclear details like ablation setups.
Key Points
- โขReviewer: experiments improved paper but details partially clarified
- โขNo specific follow-up questions provided
- โขOne additional response allowed during discussion period ending April 7
- โขFirst-time submitter requests handling strategies
๐ง Deep Insight
AI-generated analysis for this event โ not the original article.
๐ Enhanced Key Takeaways
- โขICML utilizes a structured rebuttal phase where authors are limited to a specific character count, making concise, evidence-based responses critical for addressing reviewer ambiguity.
- โขThe 'discussion period' is a formal stage in the ICML review process designed to allow reviewers to interact with authors and each other to resolve disagreements or clarify lingering concerns before final meta-review.
- โขExperienced researchers often recommend that authors proactively address vague feedback by politely asking for specific clarification while simultaneously providing a summary of the most impactful changes made to the manuscript.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
โณ Timeline
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Original source: Reddit r/MachineLearning โ
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