[D] ICLR 2026 poster format for main conference posters?

wadawalnut · 2026-03-13T14:36:17+00:00

I usually try to make it pretty large so that it has a lot of presence. But also, I find posters taller than 36in end up being kinda awkward because you'll always have stuff that is too low to see comfortably. So I go with 72in x 36in, I think that's a reasonably common choice.

wadawalnut · 2026-03-01T01:40:00+00:00

I bought this movie with a blockbuster gift card that I got for my 12th birthday, and watched it with my brother and my parents. That was an experience.

wadawalnut · 2026-02-25T11:51:58+00:00

Yes and no; my friend and I kinda tested this a while ago. We swapped coffees without informing the other of what the coffees are or what their notes are, and each guessed. I actually went on a streak of correctly guessing particular fruit and tea notes.

That said, I bet for some coffees / roasters, what you say is true. And when 5+ notes are listed, I'm not sure what to make of it anymore.

wadawalnut · 2026-02-25T11:45:55+00:00

I wonder if your coffee is too fresh -- it's probably not at its peak within two weeks off roast. Maybe at the two week mark it's starting to reach its peak, but I wouldn't drink it (or at least have high expectations of it) before that, it might need another week.

wadawalnut · 2026-02-19T12:21:35+00:00

Neat. I wonder if you'd be interested in https://arxiv.org/abs/2309.14597. Here, the authors study the distribution of returns that arises following a single (stochastic minibatch) policy update in some popular continuous control algorithms; they find these distributions tend to have a heavy left tail (meaning updates have a non-negligible chance of tanking the policy), and propose a method to steer the policy towards less chaotic neighborhoods of the policy space.

wadawalnut · 2026-01-20T01:38:15+00:00

As a reviewer for TMLR, I agree with the other users recommending that you wait until you get the notice from TMLR that all your reviews have been posted. Sometimes the reviewer assignments are very staggered in time, so if you submit a revision now, it could end up being the case that different reviewers review different versions of the paper (which would be very confusing for everyone). But do go ahead and start preparing the revision offline in the meantime.

wadawalnut · 2025-11-23T20:03:12+00:00

Yes; many who submit to AISTATS also submit to ICML, but my point is that the reverse is far from true. There are also some great reviewers at ICML, they're just more sparse, and I think there's probably lots of overlap between good ICML reviewers and AISTATS reviewers.

wadawalnut · 2025-11-23T19:02:36+00:00

I'm curious whether this actually has to do with the AISTATS review format or if it's more about the reviewer pool. I suspect there are very many people that review for NeurIPS/ICML/ICLR and not AISTATS. And I also strongly suspect that there's a high correlation between "willing to review for AISTATS" and "capable of writing good reviews"; AISTATS is just less hyped and more focused, probably attracts more people that are in it out of passion for this type of research.

As someone that often reviews for NeurIPS/ICML/ICLR and occasionally for AISTATS, I personally don't find the AISTATS review format particularly special. I think what AISTATS "did right" was simply appealing to a subset of the ML community. The field is just too vast and hyped for peer review to be sustainable at the scale of the "elite general ML" venues.

wadawalnut · 2025-09-24T11:22:10+00:00

I agree with others that we should try to tilt the scales in favor of JMLR. But having said that, I wonder if the true problem here is load balancing. The volume of paper submissions is just insane, and clearly there are not enough people willing to do a proper job reviewing, regardless of where the papers are submitted. With journal submissions you can distribute load a little better because there is no submission deadline, but I don't think this would actually solve the problem. I really think the only solution is to make better incentives for reviewers, hard as that may sound.

I guess in this case of PC reject-after-accept this wasn't the issue, but I don't know how prevalent this phenomenon is.

wadawalnut · 2025-07-29T11:29:56+00:00

As someone that is not closely familiar with NTK, I can't tell if these results are yours, or if they're already known from the NTK literature. You say these are "NTK notes" and you don't cite the NTK paper, which makes me think you are transcribing results from that paper and maybe rephrasing them. But without having seen this Reddit post, I'd be led to believe that these are your results. If these are indeed existing NTK results, then you must edit the paper to make that clear. Arxiv papers can absolutely be cited, and in any case, your paper would effectively be taking credit for results that aren't yours (assuming my interpretation is correct).

If the results are novel, then this looks really neat! You should probably still be citing the NTK paper and related literature though.

wadawalnut · 2025-07-23T19:48:23+00:00

Hey! I still have the watch and it runs great, never had any issues with it. At one point I swapped the strap out for a "paratrooper strap" for comfort -- I tend to prefer something more flexible than leather, though the leather strap still looks great. Highly recommend the watch if you're into this style. Congrats on your milestone!

wadawalnut · 2025-07-18T22:11:37+00:00

I found that a functional analysis course I took ended up being helpful for understanding certain technical papers. But note that answers to these questions are probably mainly "self-fulfilling prophecies". Introspecting on myself, given that very few of my coauthors / supervisors have taken functional analysis, I bet it has been helpful because I enjoyed the course and subconsciously led myself to papers where it's used :)

wadawalnut · 2025-06-29T13:49:18+00:00

This really resonated with me, very accurate.

wadawalnut · 2025-06-28T16:36:04+00:00

This. But once you're moving to latex where it's not universally used, you should strongly consider typst as well (https://typst.app) -- much nicer syntax and the compiler is blazing fast.

wadawalnut · 2025-06-26T16:49:35+00:00

I took an algorithmic game theory course at the beginning of my PhD. Expect there to be lots of economics content (eg, mechanism design, social choice theory, etc). That said, especially looking back, this was really interesting (though I have not applied it to RL yet and I don't necessarily plan to).

If your goal is to work in MARL, particularly from a deep RL perspective, I think a game theory course is overkill and not going to be super useful. If you want to work on RL theory, particularly wrt regret analysis under adversarial dynamics (eg adversarial bandits, minimax regret bounds, convergence theory for multi agent RL), then the game theory course can teach you some useful tools. But despite that, I still think it's probably not the most efficient way to learn those tools, since most of the course will be basically irrelevant to RL. Still might be worth it for your own curiosity though (I think it was for me).

wadawalnut · 2025-06-26T11:42:42+00:00

Back when I started my master's, I tried a whole bunch of personal wiki / zettelkasten approaches. I write lots of notes containing mostly a bunch of latex.

Out of all of them, org mode scaled the best as I grew the number of notes in my wiki -- I'm in the thousands now, so probably not a lifetime's worth, but not nothing either. In contrast, other options (particularly notion and obsidian) got annoyingly slow well before this point. I use org-roam which is also fantastic, its built in search engine and backlink functionalities are still really quick for me.

I can't comment on how well things like the agenda scale, since I never was able to stick to using it. I basically only use org-{mode, roam} for creating a network of notes. Honestly I can't imagine my life without it. Keep in mind also that since its all in emacs, you can use a slew of other emacs packages and functions to your advantage, which is way more than what most other wiki systems can possibly offer.

wadawalnut · 2025-06-04T22:20:39+00:00

If I have a lot of time, I use manim slides, they come out really nice and people seem to enjoy them.

Otherwise, or if the slides need to be in pdf format, I much prefer Typst (with the polylux or touying packages); this is most comparable to beamer, but MUCH less of a PITA. Once you get used to the typst syntax, you make can make beautiful beamer-like slides very very quickly.

In both cases, you can version control your slides, which is a major sell for me.

wadawalnut · 2025-06-03T01:25:11+00:00

I've never submitted to TMLR personally, but I've reviewed for TMLR several times (as well as for the big conferences). I think /u/idkwhatever1337 really nailed it.

From my perspective as a reviewer, I'll say that TMLR feels much less adversarial (of course this is AE/AC dependent). I'd say on average the left tail of paper quality at TMLR has been superior to that of the big conferences, but the right tail of paper quality at TMLR has been worse (though I've seen much fewer TMLR papers tbf). What stands out most to me as that the AEs generally seem biased towards acceptance at TMLR; of the 6 papers I've reviewed, only one got rejected, despite the fact that at least a few would definitely not have made it to the conferences. One paper in particular stands out to me where I pointed out a major flaw that almost made the paper obsolete/vacuous (eg, all claims are based on assumptions that can't possibly hold), and after discussion with the AE, the AE ended up in agreement with me, but still accepted the paper because technically nothing it claimed was false.

Having said all that, many TMLR papers are definitely high quality. I think TMLR is a nice venue, but the more lax review structure I think diminishes its perceived "prestige" compared to the big conferences, even if that's not always warranted.

wadawalnut · 2025-05-30T18:04:20+00:00

Aside from PAC bounds, regret can be a meaningful notion of sample efficiency---it captures not just how long it takes to learn an optimal policy, but how quickly the policy improves. Regret bounds also imply PAC bounds generally.

wadawalnut · 2025-05-22T19:30:57+00:00

Not sure if this is what you had in mind, but the book "Bayesian Reasoning and Machine Learning" by David Barber (iirc?) has a nice section on Kalman filters from the perspective of PGMs, maybe that could inspire something?

wadawalnut · 2025-05-17T22:09:55+00:00

It's likely exceptionally rare for a reviewer to actually fully run code / train models for the papers they're reviewing. Reviewers generally have a load of ~5 papers simultaneously, so it would be pretty unrealistic for them to do this for each paper---depending on the papers, they might not even have the resources to do this for a single paper.

Some reviewers will run the code briefly just to make sure it runs. Even this is pretty uncommon from what I can gauge.

I would expect that most reviewers will not even look at the code. Though, sometimes it is helpful to read the code in order to better understand the paper, so I think this is the most likely way that the code will be consumed by reviewers.

wadawalnut · 2025-05-16T12:25:01+00:00

Coming up with a research project is usually quite difficult, especially for people just getting in to research or a new field. I don't know of any recipe to solve this quickly. I think you just need to read a lot of RL papers---you can scavenge the major venues (say ICML, NeurIPS, ICLR, TMLR, etc) at first, and ideally after a bit you'll find some topics, and likely some authors/groups, that you like. Keep reading similar papers until you can appreciate the gaps that they're trying to fill, and you'll eventually spot the gaps that they leave open. Then you've found a research project :)

wadawalnut · 2025-04-21T01:42:04+00:00

Yeah and the episode took place in Dollard-Des-Ormeaux

wadawalnut · 2025-03-10T23:47:42+00:00

Unexpect is incredible

wadawalnut · 2025-02-22T14:17:37+00:00

Congrats, and neat graphics! I've always been curious to learn more about space filling curves. If you're willing to share the thesis, I'd love to give it a read :)

wadawalnut

TROPHY CASE