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Will Carnegie Mellon be the top university accepted to NeurIPS 2019?
NeurIPS is widely regarded as the leading conference in machine learning. Held every year, it attracts large numbers of attendees, causing tickets to sell out within twelve minutes last year. Around the world, ML researchers furiously scramble to prepare papers in time to submit, as NeurIPS is also regarded as the most prestigious publication venue in ML.
Each year, once the list of accepted papers is released, people invariably calculate which institutions and authors have contributed the most papers, or the most first authorships. One relatively easy-to-check stat is for each institution, how many accepted papers have an author affiliated with that institution. In 2017 (when the conference was called NIPS) and 2018, the top institution was Google Research, by a large margin. However, the university rankings are somewhat more competitive: in 2017, Carnegie Mellon (CMU) was at the top, while in 2018, MIT took the lead. CMU also does well at the other leading ML conference, ICML, where it took top or equal top spot among universities in both 2017 and 2018.
In this question, we ask:
Will CMU be the university with the largest number of papers accepted to NeurIPS 2019 with at least one author affiliation?
Note that the question will resolve yes if CMU is tied for first place on this metric. Question closes when NeurIPS 2019 submissions are due, and resolves when a list of accepted papers with author and affiliation information is released. [N.B. these dates have not yet been publicly released, current close and resolve date is a best guess]
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Use the community stats to get a better sense of the community consensus (or lack thereof) for this question. Sometimes people have wildly different ideas about the likely outcomes, and sometimes people are in close agreement. There are even times when the community seems very certain of uncertainty, like when everyone agrees that event is only 50% likely to happen.
When you make a prediction, check the community stats to see where you land. If your prediction is an outlier, might there be something you're overlooking that others have seen? Or do you have special insight that others are lacking? Either way, it might be a good idea to join the discussion in the comments.
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