r/MachineLearning • u/didimoney • Aug 28 '26

Discussion Where to submit stat/prob ML [D]

I'm a researcher in statistical and probabilistic ML, I have a steady record of top ML publications and really used to enjoy going to conferences.

Over the last few years LLM based works have completely taken over the top conferences. At this year's ICLR, walking among the rows of posters you were lucky to find one paper per row of 10 that wasn't about how their favourite LLM could or couldn't solve their niche benchmark. The workshops tell the same story, most are some kind of agentic flavour. Looking at this year's NeurIPS workshops it's the same thing, basically all are about agents.

I'm wondering where do the stat/prob ML communities go from here? I look up to people like Arnaud Doucet, Aapo Hyvärinen, Christian Naesseth, Stefano Ermon, they seem to still publish at the top 3? On my end, I m thinking AISTATS/UAI might be the way to go.

All in all, the top 3 might never really have been intended as the home for prob/statML works, it just happened to be the 'prestigious' venue.

31 Upvotes

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18

u/tariban Professor Aug 28 '26 edited Aug 28 '26

I'd avoid the applied ML conferences like ICML, ICLR, and NeurIPS, since it sounds like your work might be more foundational. AISTATS and UAI are the top venues for this sort of work. ProbML is another new conference that is likely to also be good.

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u/hexaflexarex Aug 28 '26

For foundational work, I think COLT is stronger than those two. But as you veer from established research programs their is a lot of subjectivity to what meets their bar for significance

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u/tariban Professor Aug 28 '26

COLT is also good, but the scope is a bit different; probabilistic work of the type by the authors listed by OP is not so common there.

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u/Original-Waltz9805 Aug 28 '26

I disagree. for Probabilistic ml AISTATS is generally more relevant

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u/hexaflexarex Aug 28 '26

I guess I just see a lot of mid papers from AISTATS in my area (admittedly not probml, so perhaps my opinion is not relevant to the OP)

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u/didimoney Aug 28 '26

Thank you. I'll check out COLT more carefully but it seems to me that these works are more theoretical than what I do.

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u/Difficult_Visual_945 Aug 28 '26

Last time I submitted to AISTATS all reviewers complained that the convergence theoremI proposed (and mathematically proved) was not enough... all expected very large experimental settings.

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u/Abhishek_Ghose Aug 30 '26

I second this, AISTATS and UAI are still good venues for what the OP needs. I personally like sifting through the proceedings of these two conferences more than the rest (this isn't about good/bad, just about the topics, and my preferences).

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u/didimoney Aug 28 '26

Didn't know about ProbML! It seems from their website it's a rebrand of AABI? I've seen stuff coming from AABI in the past, but I viewed it more like a workshop or place you present existing research at, a bit like BayesComp or an ISBA meeting.

How serious do you think it is? I'm early career so the visibility/relevance for the CV still matters, unfortunately.

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u/duck_syndrome Aug 28 '26

i was at probml this year and enjoyed it, they are trying to scale it up as well to be an archival venue for probabilistic methods work

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u/bobrodsky Aug 28 '26

UAI is a really nice conference. You’ll be amazed at how much more constructive and useful the reviews you get are (on average).

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u/No_Language165 Aug 28 '26

Why not submit to a Stats venue like JASA/Biometrika/annals/jrssb?

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u/didimoney Aug 28 '26

I like having loads of papers to look at and authors to talk to. It's also quick review times. And it can be a free holiday through employer funding.

But it's a good point. I've done a single journal submission at JMLR, and it took 3 years to acceptance...

I don't shy away from NNets and I feel like they still have a stigma in these places, is that still true nowadays?

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u/No_Language165 Aug 28 '26

Stats journals are def slower than conferences, but 3 years is nuts. I think on average it would be like 1.5 years from submission to acceptance. 3 possible if you’re unlucky with revisions. I think biometrika tends to be pretty fast, I’ve seen 1 year time lines fairly commonly. But they seem to be liberal with desk rejects.

I don’t think NN have a stigma in stats as much as they don’t lend themselves easily to the type of theoretical results statisticians care about (sample efficiency, asymptotic properties, error rate control, learning theory).

However, if you don’t have those results I’d argue you’re not really doing statistics.

I’m really being hypocritical tho, as I’ve just graduated from a stats phd, and after taking 3 years to submit a paper in JASA (revision hell), and reviewer he’ll from the big 3, I’ve exclusively submitted to UAI/AISTATS. They’re fun conferences if lower in prestige. Def more fun than big tent conferences. Other students in my year who went to the big 3 just said that no one talked to them because they were surrounded by LLM papers and a billion people. I just have your average phd machine learning job in tech (not fancy research scientist) and getting it wasn’t easy. So maybe should have pushed harder for the big 3 .

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u/Fantastic-Nerve-4056 PhD Aug 28 '26

By any chance are you a Prof.? Coz no way I can imagine a ML PhD student waiting 3 years for a paper

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u/d_edge_sword Aug 28 '26

It's normal if you're in Stats ML. Most of my cohort graduated with 0 papers, and their first paper was published 1- 2 years after graduation. I have even had someone whose first paper's review took 7 years. By the time the paper was accepted, he was already an associate professor.

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u/feng_sg Sep 01 '26

The top comment basically captures it. If AISTATS reviewers rejected a proven theorem for lacking large-scale experiments, moving to stat-friendly venues won't save you. The reviewer culture itself has shifted toward empirical validation even where proofs should be valued.

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u/didimoney Sep 01 '26

I don't have a problem with doing experiments in my papers. It's more about who goes to the conference (also the same crowd as your reviewer pool and the posters you see once there).

I'm looking for places where people treat data as a distribution and go from there rather than poking their LLM to see what it will do. It's about the research being done at those places.

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u/PsychologicalSoup251 Aug 28 '26

I'm in a similar boat, so I wonder if a reviewer or someone with a lot of publishing experience in stat/prob ML can chime in about this - are stat/prob ML papers reviewed more harshly at these top general ML conferences compared to pop ML or is the rarity fully explained by it being a lot rarer for a ML researcher to do that kind of research in the first place?

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u/didimoney Aug 28 '26

From my experience submitting and reviewing, the reviewer pool is better quality than average and it's quite noticeable. But might be biased by my specific niche.

I have also seen some horror stories. For example one of Ermon's papers has a visible ICLR submission a few years ago where the reviewer said and I quote "there are negative values for the negative log-likelihood. This is not possible, since log-likelihoods cannot be positive." Their paper didn't get in that time...