English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Mainstream machine learning conferences have seen a dramatic increase in thenumber of participants, along with a growing range of perspectives, in recentyears. Members of the machine learning community are likely to overhearallegations ranging from randomness of acceptance decisions to institutionalbias. In this work, we critically analyze the review process through acomprehensive study of papers submitted to ICLR between 2017 and 2020. Wequantify reproducibility/randomness in review scores and acceptance decisions,and examine whether scores correlate with paper impact. Our findings suggeststrong institutional bias in accept/reject decisions, even after controllingfor paper quality. Furthermore, we find evidence for a gender gap, with femaleauthors receiving lower scores, lower acceptance rates, and fewer citations perpaper than their male counterparts. We conclude our work with recommendationsfor future conference organizers.

An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process