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> > You had to explain everything and make sure to refence the Big boys you were close to

> That sounds problematic

Bingo, it is problematic. It's part of how "the Big boys" get their academic and compensation (investment in their firms) rewards. You have to play the publish-or-perish, paper rank game to get ahead and stay ahead in academia, and this leads to all sorts of problems. Authors don't want to question "the Big boys" because that leads to their papers not getting published because the "the Big boys" and their bootlicker wannabes are the reviewers and they will exact their tribute. Make it to "the Big boys" club and now you're a gatekeeper and now you're also responsible for perpetuating this system.

It's why Nature-style peer review needs to become a thing of the past.

I'm not saying that "popular" (for a value of "popular" that involves peers at large, not the public at large) peer review is / will be without problems. But it seems to me that it will -at least for a while- be less corruptible.

Yeah, this is a hill I will die on. I love researching, but once I grab my PhD I do not plan to push to journals/conferences unless it is requested in a job. It just holds no meaning and I'm tired of pretending it does. Perpetuating the system harms my fellow researchers, kills innovation, and just kicks the growing can down the road.

The other hill I will die on is that we shouldn't refer to journal/conference publishing as "peer review." This is one form of peer review, but there are MANY more. And as far as I'm concerned, 3 randos that briefly skim my paper in an adversarial setting (zero sum) looking to reject works barely constitutes peer review. Peer review is what happens when your peers read your work, test it, build upon it, replicate it, etc. We need to stop this language because it helps no one.

> Peer review is what happens when your peers ...

I would add: when your peers' review commentary is public.

I love when this happens tbh, but it is rather rare. Often I can tell that a paper was rejected for dumb reasons and that gives me a good signal to actually read it.

Yeah, most of peer review is private

If journals posted first-submission comments for published papers I think there's be a radical change in comments. For good or ill, I have no idea.

> I get 3 reviewers and 3 completely different reasons to reject, often nonsensical

Could one problem with these kinds of reviews be that the reviewers have absolutely zero skin in the game? Like, you can write complete nonsense and nobody will blame you personally for it, you have no stake in the outcome of the submission/paper what so ever, and you're much more incentivized to come up with reasons to reject than to say "good enough". I realize the latter part can be good in theory, as it sets a high bar, but it also often feels like this goes awry when not kept in check somehow.

> Could one problem with these kinds of reviews be that the reviewers have absolutely zero skin in the game?

Actually, it is worse. They probably have skin in the game, and incentives to reject! Most CS fields publish through a conference system. This is a zero or one shot system (if you get a rebuttal phase, _if_ you want to count that). These conferences have acceptance rates that they "need to maintain" to keep their rankings. In other words, you are competing against all other papers being submitted to the same conference, not just papers with similar topics. Even if it is only a little, rejecting a paper actually increases the odds that your submission makes it through.

But yeah, you're in the right ball park. This is also why you will quite frequently see conference review guidelines blatantly violated and why you will see area chairs and metareviewers just not care. They all have incentives to encourage rejection, let alone be impartial.

I think the system works when the community is small and there is accountability among peers. Accountability creates a dampening effect on bad behavior. But at scale, you only need a few bad actors to setup a feedback system and to not just spoil the entire barrel, but the entire shipment.

Edit: I should also add that there's an additional negative incentive. You are not judged by how often you review, how many reviews you perform, or how good your review is (hard to measure). So reviewing ends up taking away time from the very limited time you have to do work that you are actually evaluated on. This is likely why reviews are so rushed. There's a feedback loop too, since many will see that others rush when reviewing their work so they get tired and end up rushing when reviewing the work of others. "If they aren't going to give my work their time, why should I have their work my time?" thinking grows.

>> I don't feel lucky, I feel frustrated. I get 3 reviewers and 3 completely different reasons to reject, often nonsensical (not joking, recently was rejected because a broken reference link to the appendix).

The people who review your papers in conferences and ask you why you didn't cite future arxiv papers are the same people who put their work on arxiv and cite each other's preprints. You can't rely on the process of "peer-review" on arxiv any more than you can rely on the conference peer-reviews because they're performed by the same people, and they're people who don't know what they're doing.

The sad truth is that the vast majority of the researchers in the machine learning community haven't got a clue what the hell they're doing, nor do they understand what anyone else is doing. The typical machine learning paper is poorly motivated, vaguely written, and makes no claims, nor presents any results, other than "our system beats some other systems". As to reproducibility, hell if we know whether any of that work is really reproducible. Everybody who references it ends up doing something completely different anyway and they just cite prior work as an excuse to avoid doing their job and properly motivating their work. The people who write those papers eventually get to be reviewers (by sheer luck), or sub-reviewers. They have no idea how to write a good paper, so they have no idea how to write a good review, either. And they couldn't recognise a good paper if it jumped up and bit them in the cojones.

I love to cite Geoff Hinton on this one:

  GH: One big challenge the community faces is that if you want to get a paper
  published in machine learning now it's got to have a table in it, with all
  these different data sets across the top, and all these different methods
  along the side, and your method has to look like the best one. If it doesn’t
  look like that, it’s hard to get published. I don't think that's encouraging
  people to think about radically new ideas.
  
  Now if you send in a paper that has a radically new idea, there's no chance
  in hell it will get accepted, because it's going to get some junior reviewer
  who doesn't understand it. Or it’s going to get a senior reviewer who's
  trying to review too many papers and doesn't understand it first time round
  and assumes it must be nonsense. Anything that makes the brain hurt is not
  going to get accepted. And I think that's really bad.

https://www.wired.com/story/googles-ai-guru-computers-think-...

So the problem is not arxiv or not arxiv, the problem is that peers in peer review lack expertise and knowledge and they can't do their job well.

As to your very last point, it isn’t my “job”. It’s yet another task that I take on for no recognition, nor additional pay - as is much of academic life.

Jobs come with a lot of shitty aspects. Don't get me wrong, I generally don't enjoy reviewing either. But I put a lot of work into it because regardless of what I think, this has a significant effect on real people and their entire livelihoods can depend on this task. Especially those in their early career. One or two publications in a top tier journal can land them that internship or job which snowballs.

So I'd ask you do one of two things, either:

- Review a work with the diligence and care that you wish someone would give to you

or

- Don't review

I'd also appreciate it if you openly recognized how stochastic the system is and that if/when you become in a position where you need to evaluate someone, that you remember this and take it into consideration. It has a lot of value to you too, since if the metric is extremely noisy it doesn't provide you value to heavily rely upon that metric. Look for others.

I do see it as my job, and my responsibility. I also see it as my job to help more junior colleagues, and even to teach what I've learned to undergraduates. I don't want to do any of those things. I don't even want to write papers. I just want to sit on my couch, code, and discover new knowledge that blows my mind.

But, people pay me to do a job. It's not in my contract in any clear terms, but to do my job well I need to do all those things; and I like doing my job well.

> The people who review your papers in conferences and ask you why you didn't cite future arxiv papers are the same people who put their work on arxiv and cite each other's preprints.

I don't have a problem citing arxiv works. I mean I have no faith in the official system, so why wouldn't I? But this is a clear indication of an impossible bar to pass and demonstrates the ridiculousness of the system. It isn't just that there's a malicious or dumb reviewer, it is that the other reviewers don't call them out, the AC doesn't call them out, and the metareviewer doesn't call them out. The best case was that broken link person and the AC made them update their response, but this was in the response to my 1 page rebuttal (for 4 reviewers) where they got to say whatever they wanted and I had no chance of response. (Their initial review was literally 2 lines) I don't know how anyone can see this and not think that the system absolutely failed at every level. You're probably unsurprised to hear that this is a common occurrence rather than uncommon.

> the vast majority of the researchers in the machine learning community haven't got a clue what the hell they're doing

I'm well aware. __I__ have no idea what I'm doing, but at least I can tell you the difference between probability and likelihood or that tuning on the test set is information leakage. Research often requires venturing into the unknown and unexplored. That's fine. I don't care if we're all stumbling around in the dark. I do care when people are not just unable to admit it, but unable to recognize this. But that's the classic "tell a lie enough times and you'll start to believe it" situation.

I am in full support of that Hinton quote. This is the first I've heard it (or recall at least), but I often say quite similar things (in fact, just did in another thread). I do mean it when I say that our current system harms us and I'm confident that we won't get to AGI with this system.

> the problem is that peers in peer review lack expertise and knowledge and they can't do their job well.

I won't disagree with this point, but I believe that this is a systematic problem rather than an individual. The system encourages this behavior rather than stamps it out. So in that sense, I think people are doing their job very well. It is just that I don't think their job actually aligns with the intent of the job. Classic case of irony, that the group of people that highly discusses alignment is one of the worst at this. But I guess we shouldn't be surprised given that lately we've seen how unethical we've seen people who write about ethics are.

I do want to add one thing though. A good paper is hard to recognize. A bad paper is easy, but a good paper may be indistinguishable from a bad paper. This is the "paradox" of research and something people need to take to heart. That is if we want to align our job descriptions with our actual jobs.

>> Research often requires venturing into the unknown and unexplored.

Oh, in that sense I'm clueless too. We all are. What I'm pointing out though is that many people in machine learning research don't really understand what's the point of research, or even what's the point of a research paper. They copy each other's writing style and produce the same kind of low-information-content paper.

There's been a huge influx of new people to machine learning research in recent ish years. I think it started around 2012 and the ImageNet results. I did my Master's in 2014-15 (part-time) and I was surprised by the number of people in the machine learning class I took, and how many of them had nothing to do with computer science or AI, and were instead coming from backgrounds in business or more rarely science. At least I'd expect the people with the science backgrounds to know how to write a paper, but it seems most people who come to machine learning from, say, physics or biology, bring with them a facility with calculus and continuous mathematics, but not much in terms of scientific methodology.

Hinton hints at that in the rest of his comment I didn't quote:

What we should be going for, particularly in the basic science conferences, is radically new ideas. Because we know a radically new idea in the long run is going to be much more influential than a tiny improvement. That's I think the main downside of the fact that we've got this inversion now, where you've got a few senior guys and a gazillion young guys.

Well I guess the "gazillion young guys" need a bit of time to figure out how to write good papers, and review them.

>> I do want to add one thing though. A good paper is hard to recognize. A bad paper is easy, but a good paper may be indistinguishable from a bad paper. This is the "paradox" of research and something people need to take to heart. That is if we want to align our job descriptions with our actual jobs.

That's true, and part of the reason why reviewing is hard work. I still find it very hard to reject a paper. What if I'm the one who doesn't get it?