We've updated the working def :)

https://iclr.cc/Conferences/2023/CallForTinyPapers

Very valid point. Working on it (and also thinking to add some others, too).

Changed. Thanks for the suggestion!

Probably soon? Or at camera ready stage?

I found it by logging in. Maybe they aren't public yet?

Ahh, I had to log in to see the meta review.

Can you see your meta review?

I'm not seeing area chair meta reviews or decisions on OpenReview yet. Anyone see them on their papers? I got an email only.

That sucks. Hopefully you can correct their misconceptions or at the very least set the record straight.

What makes you unsatisfied? Your scores seem on the positive side.

Yes.

Edit: I don't believe the image of the poster itself is included, but rather the paper!

You're welcome and good luck :)

No one can tell you for sure at this stage, and ultimately it's a case-by-case decision. However, you can see what past years' scores led to in terms of decisions here:

https://www.reddit.com/r/MachineLearning/comments/ymctqy/comment/iv3m9qy/?utm_source=reddit&utm_medium=web2x&context=3

At the very least, since the reviews and your replies are public, you can try to correct the record re misconceptions, even if you don't end up changing certain reviewers' minds. And sometimes when replying to one reviewer you can convince a different reviewer. Good luck.

Congrats on your first submission! Sounds pretty close. Good luck.

>How should I read the reviews?

I suggest reading them in full multiple times, then trying to write a synthesis of the opinions and main critiques. After that, you need to figure out how will you respond to the main critiques: will you try to fix them (if so, how?) or are those critiques incorrect or mistaken in some way (if so, how?).

​

>So how good is accepted?

No one can tell you for sure at this stage, and ultimately it's a case-by-case decision. However, you can see what past years' scores led to in terms of decisions here:

https://www.reddit.com/r/MachineLearning/comments/ymctqy/comment/iv3m9qy/?utm_source=reddit&utm_medium=web2x&context=3

>The last one made two serious mistakes (and one of them is undergrad-level CS stuff)

That sucks!

Good luck, though. I think the confidence ratings are not always too meaningful. There might be a chance you can correct the mistake and/or AC notices and decides in your favour.

>2 8s and 3 3s

For a total of 5 reviews? Not only a big split but also a large number (both of my submissions only have 3 reviews, and average across all papers is apparently 4.1 at present).

Congrats on your first submission :)

>8, 3, and 3

Very big split! In one past case of mine, I found the higher-rated one sneaked their rating down to go along with the group without saying anything. Hope that doesn't happen for you.

>I'm curious if reviewers can update their score freely during the discussion or they can only update once at the end

AFAIK they can change at any time by "editing" their original review comment. Ideally they also add some text/reply to indicate that they changed their score and why.

Whatever the outcome, congrats on your first submission!

>I've never heard about delayed reviews before for ML venues

ICML were delayed in 2022.

FYI, if you are curious about your chances given your ICLR scores, you can check some scores (and resulting decisions) from previous years:

2022: https://guoqiangwei.xyz/iclr2022_stats/iclr2022_submissions.html

2021: https://github.com/evanzd/ICLR2021-OpenReviewData

And someone even made a small calculator based on 2019 data: http://horace.io/willmypaperbeaccepted/

Edit: Someone has also scraped this year's data, so you can see where your paper lies in the distribution: https://docs.google.com/spreadsheets/d/1INZI9epkfBkPOlKuJFaffUCOKDns87Iqg4zovHnf-zs/edit#gid=554805545

A lot of popular ML content (like ML papers/marketing) is hot air. It's sort of a systemic issue.

>NeurIPS has some pretty technical mathy papers too, right?

Examples?

>COLT is a good place to start.

Although it basically limited to optimization/stats, not math.

>Overall heavily mathematical papers, when properly contextualized and given intuitive understanding of proofs tend to be very popular.

Strongly disagree. MLers have a very limited appreciation of 'math'.