•1 min read•from Machine Learning
Post Rebuttal ICML Average Scores? [D]
Our take
Navigating the post-rebuttal landscape of ICML average scores can be frustrating, especially when unexpected reviewer comments arise. In this case, a score of 3.5 reflects a mix of feedback, but it's disheartening when a reviewer introduces a new concern that lacks prior context. This situation can leave authors feeling perplexed, particularly when tools like Paper Co-Pilot suggest that a score of 4.2 signifies top-tier status. How can we better interpret these scores and improve our submissions in such a competitive landscape?
I have an average of 3.5. One of the reviewer gave us a 2 by bringing up a new issue he hadn't mentioned in his initial review, taking that from another reviewer's concerns. The reviewer he took it from already mentioned that it isn't an actual issue too.
Paper Co-Pilot is driving me crazy, apparently 4.2 is just the top 40% of papers according to it.
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