lixin4ever/conference-acceptance-rate

This repository is a reference collection of acceptance rates for major AI research conferences going back to roughly 2012. Acceptance rate is the percentage of submitted research papers that a conference agrees to publish, which is a common proxy for how competitive or selective a venue is. The data is organized by research area: natural language processing conferences (ACL, EMNLP, NAACL, COLING), computer vision conferences (CVPR, ICCV, ECCV), machine learning and learning theory conferences (NeurIPS, ICML, ICLR, COLT, AISTATS, UAI), general AI conferences (AAAI, IJCAI), and data mining conferences (KDD, WWW, SIGIR, and others). For each conference edition, the entry shows the acceptance percentage, the number of accepted papers, and the total number of submissions. Where conferences distinguish between long papers and short papers, both figures are listed separately. The repository also includes a chart image showing acceptance rate trends over time across these venues. A Jupyter Notebook is included, which suggests the data may also be available in a format that can be queried or visualized programmatically, though the README itself is primarily the tables. The audience for this data is researchers deciding where to submit their work, students curious about competition levels at different venues, or anyone tracking how AI conference submission volumes have grown over time. Submission counts at venues like NeurIPS and CVPR have roughly tripled over the decade shown, while acceptance rates at most conferences have stayed in the 20-30 percent range.