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No. People who publish in these conferences do not consider these ML conferences. Historically computer vision and NLP started out as fully distinct communities with almost no overlap with the ML community. Since about 2014 and the deep learning revolution, the lines have been blurred a bit, but they are still very distinct communities.
NeurIPS/ICML are basically considered the same conference, and any paper that could be accepted at one could also be accepted at the other without modification (beyond styling); the only meaningful difference is the submission deadline. Similarly, CVPR/ICCV/ECCV are all basically the same conference with difference submission deadlines, and ACL/EMNLP/NAACL. You cannot, for example, take a paper designed for NeurIPS/ICML and get it published at CVPR/ICCV/ECCV without major structural changes, and that's we know they are part of different communities.
The division here is not academic/industry like you suggest. Bishop---who again is the prototypical author for probabilistic ML---works at Microsoft and you can find the textbook info at: https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/. The division is based on the conference communities and who publishes/reviews where.
Honestly, those papers shouldn't take 8 hours for a researcher to read. I had a pretty solid idea of what they were doing in <5 minutes, and I'd guess in <1hr I could fully understand everything about each paper.
The difference is that I am the target audience. Having done a ML phd, I've read >20 graduate level textbooks cover-to-cover and >1000 papers in great depth. If you haven't done this background work (which is fine---it's not for everybody, and I actively recommend my students not pursue this path) then these papers are not designed for you. You should accept this rather that complain that they are too hard or gatekeeping.
Ok I can grant that the conference communities are distinct. But I do think there is still some difference between those two papers and papers like these: CATs and DAGs: https://arxiv.org/abs/2410.14485, Distributed Alignment Search: https://arxiv.org/abs/2303.02536. I had a solid idea of what these papers were doing in ~10 minutes of reading them. But it took me several hours each to understand the Prior-Fitted Networks, then the math paper behind it, then the Causal Prior-Fitted Networks and lastly the Causal Foundation Models with partial information, at a level that I could apply or implement them. If you are saying that's because I am >20 textbooks and >1k papers behind on the background understanding, then sure I can't argue with that. But I could implement CATs and DAGs and DAS within the hour of reading them.
If I am not the target audience for any of them, how come some are more approachable from a practical side and others are not? I would expect truly being an outsider to have a roughly even level of penetration on the topics, not a wildly disparate one. To me that is indicative of a level of quality, skill on the author, or even writing to a wider audience (pretty much a skill) instead of writing to the clique (poor intent).
"Too" is a load bearing word that I don't think I have used. My complaint is some of these are fairly arcane, use complicated math to obfuscate a straightforward idea, or are deliberately being written in a way to gatekeep.
Some industry labs are essentially better paid academic labs, and attract prestigious academics. Google, Deepmind, FAIR, Microsoft, and plenty of small startups targeting PhD students. OpenAI started out that way. Other industry labs/research companies are far more implementation focused, in fact I'd say the majority are the latter. You'll get PhDs but also ML Engineers (like me) who transitioned as time/interest went on. And yeah we publish in the CVPRs and ACLs. ImageNet was a CVPR publication, AlexNet was a Nature publication, Resnet (major introduction of skip connections used everywhere in ML now) was CVPR. If you'd like to say those aren't "ML" then I'd say you definitely have a purity issue, if you want to grant them access to the vaulted halls, then its farcical to say that CVPR is not part of the ML community (doesn't need to be exclusively, I'd grant that the CV community and the ML community have not always had overlap)
EDIT: After mulling this over, you've actually planted the idea that many ML academics are essentially writing pointless papers that explore some weird theoretical idea but have no desire for that paper or theory to even get implemented in anything besides a toy problem. In essence, they are slop. And because they are circle-jerking slop papers, they are written for other circle-jerking slop authors in the wider ML community.
I don't see anything wrong with writing papers for a "clique" when you are actively trying to help people come into the clique who want to. The ML community has pioneered open access to papers via JMLR/ICML/NeurIPS breaking away from the older venues in the 1980s that refused open access, and basically every graduate level textbook is available for free online.
I basically agree with everything in your last paragraph. Except that AlexNet was published at NeurIPS, not Nature: https://papers.nips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
Damn Google's AI overview led me astray. Good old NIPs was the publication. (Yes I know they changed their name to not be a slur). The other two I knew because I read them in undergrad. I could not remember AlexNet's OG home.
We might have different definitions of "actively". Yes they are open, but strictly classifying ML as a very specific thing is not. It still looks to me like gatekeeping a very specific boundary that is not reflective of the wider reality
If you seriously feel that the ML community is gatekeeping, then I invite you to come join the community and propose ways to remove these gates. There are regular workshops hosted to address these issues and improve them. In just 4 days, there will be a workshop on "The Future of Machine Learning Publishing" https://inverseprobability.com/sorrento2026/future-ml-publishing.html.
There are also more-or-less annually workshops at NeurIPS/ICML on improving the publishing process in ML. Here is an (incomplete) ChatGPT generated list:
(2010) : https://mloss-static.ml.tu-berlin.de/workshop/icml10/
(2018) : https://ml-critique-correct.github.io/
(2019) : https://ml-retrospectives.github.io/
(2020) : https://ml-retrospectives.github.io/neurips2020/
(2021) : https://neurips.cc/virtual/2021/workshop/21885
(2022) : https://ml-eval.github.io/
(2023) : https://sites.google.com/view/reconsidering-peer-review
I don't know of any academic communities that are remotely as open and accessible as the NeurIPS/ICML community. The NLP and CV communities have made some progress in these directions (due to the overlap of their members and the ML community), but even other branches of CS are way behind.
Thank you for the invitation, let me see which ones I could attend. Do you know if the Neurips/ICML host a professional society like IEEE or AAAI?
They don't; it's all informal. AAAI is the closest thing and has a lot of overlap. Basically no one is a member if IEEE or ACM.
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Can I just insert this is one of the most respectful back-and-forths between ppl disagreeing that I've read in a while here between you and @PokerPirate. And about a field I know nothing about (Statistics I know, but not Machine Learning). A lesson to us all.
I'd like to think its because this is a very niche argument about where the boundary on one field is vs another. It's not really culture war material, its not an existential moral argument. Its pretty much two nerds disagreeing about what constitutes ML research, and and on a meta level, the telos of the modal ML research paper.
This forum has devolved a bit on the civil discourse front when it comes to culture wars, sign of the times I guess.
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