Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment

5 June 2023

Papers citing "Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment"

8 / 8 papers shown

Title
Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards Jaeho Kim Yunseok Lee Seulki Lee 41 0 0 08 May 2025
Paper Copilot: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process Jing Yang 56 0 0 02 Feb 2025
Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings Mojtaba Yousefi Jack Collins 27 0 0 12 Oct 2024
On the Rigour of Scientific Writing: Criteria, Analysis, and Insights Joseph James Chenghao Xiao Yucheng Li Chenghua Lin 22 1 0 07 Oct 2024
IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback Kevin Pu K. J. Kevin Feng Tovi Grossman Tom Hope Bhavana Dalvi Mishra Matt Latzke Jonathan Bragg Joseph Chee Chang Pao Siangliulue 29 10 0 05 Oct 2024
The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review Buxin Su Jiayao Zhang Natalie Collina Yuling Yan Didong Li Kyunghyun Cho Jianqing Fan Aaron Roth Weijie J. Su 19 4 0 24 Aug 2024
(De)Noise: Moderating the Inconsistency Between Human Decision-Makers Nina Grgić-Hlavca Junaid Ali Krishna P. Gummadi Jennifer Wortman Vaughan 44 1 0 15 Jul 2024
Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment Corinna Cortes Neil D. Lawrence 37 43 0 20 Sep 2021