Scientific publications significantly impact academic-related decisions in computer science, where top-tier conferences are particularly influential. However, efforts required to produce a publication differ drastically across various subfields. While existing citation-based studies compare venues within areas, cross-area comparisons remain challenging due to differing publication volumes and citation practices. To address this gap, we introduce the concept of ICLR points, defined as the average effort required to produce one publication at top-tier machine learning conferences such as ICLR, ICML, and NeurIPS. Leveraging comprehensive publication data from DBLP (2019--2023) and faculty information from CSRankings, we quantitatively measure and compare the average publication effort across 27 computer science sub-areas. Our analysis reveals significant differences in average publication effort, validating anecdotal perceptions: systems conferences generally require more effort per publication than AI conferences. We further demonstrate the utility of the ICLR points metric by evaluating publication records of current faculties and recent faculty candidates. Our findings highlight how using this metric enables more meaningful cross-area comparisons in academic evaluation processes. Lastly, we discuss the metric's limitations and caution against its misuse, emphasizing the necessity of holistic assessment criteria beyond publication metrics alone.

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