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Reposted by Csaba Szepesvari

ARLET @arletworkshop.bsky.social · 28/07/2025

Stay tuned for updates by following us or the organizers: Alberto Metelli, @antoine-mln.bsky.social , Dirk van der Hoeven, Felix Berkenkamp, Francesco Trovò, @gioramponi.bsky.social Marco Mussi, @skiandsolve.bsky.social , and @tillfreihaut.bsky.social

arlet-workshop.github.io

ARLET

A simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design.

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Csaba Szepesvari @skiandsolve.bsky.social · 10/07/2025

..actually, not only standard notation, but also to be able to speak about the loss (=log-loss) used to train today's LLMs.

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Csaba Szepesvari @skiandsolve.bsky.social · 10/07/2025

No, it is not information retrieval. It is deducing new things from old things. You can do this by running a blind breadth-first (unintelligent) search producing all proofs of all possible statements. Just don't want errors. But this is not retrieval. It is computation.

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Csaba Szepesvari @skiandsolve.bsky.social · 10/07/2025

Of course approximations are useful. The paper is narrowly focused on deductive reasoning which seem to require the exactness we talk about. The point is that regardless of whether you use quantum mechanics or the Newtonian one, you don't want your derivations mistake-ridden.

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Csaba Szepesvari @skiandsolve.bsky.social · 10/07/2025

Worst-case vs. average case: yes! But I would not necessarily connect these to minimax vs. Bayes.

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Csaba Szepesvari @skiandsolve.bsky.social · 10/07/2025

Yeah, admittedly, not a focus point of the paper. How about if the model produces a single response, the loss is the zero-one loss. Then the model better choose the label with the highest probability label, which is OK. Point of having mu: Not much point, just matching standard notation..

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Csaba Szepesvari @skiandsolve.bsky.social · 10/07/2025

I am curious about these examples.. (and yes, I can construct a few, too, but I want to add more)

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Csaba Szepesvari @skiandsolve.bsky.social · 10/07/2025

No, this is not correct: Learning 1[A>B] interestingly has the same complexity (provably). This is because 1[A>B] is in the "orbit" of 1[A>=B]. So the symmetric learning who is being taught 1[A>B] need to figure out it is not taught 1[A>=B].

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Csaba Szepesvari @skiandsolve.bsky.social · 09/07/2025

Maybe. I am asking for much less here from the machines. I am asking for them just to be correct (or stay silent). No intelligence, just good old fashioned computation.

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Csaba Szepesvari @skiandsolve.bsky.social · 09/07/2025

Yes, transformers do not have "working memory". Also, I don't believe in that using them in AR mode is powerful enough for challenging problems. In a way, without "working memory", external "loop", we say the model should solve problems by free association ad infinitum or at least until

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Csaba Szepesvari @skiandsolve.bsky.social · 09/07/2025

On the paper: Interesting but indeed there is little in common. On the problem studied in the paper: Would not a slightly more general statistical framework solve your problem? Ie measure error differently than through the prediction loss (AR models: parameters, spectral measure, etc.).

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Csaba Szepesvari @skiandsolve.bsky.social · 09/07/2025

Yeah, I don't see the exactness happening that much on its own through statistical learning. Neither experimentally, nor theoretically. We have an example for illustrating this: use the uniform distribution for good coverage, teach transformers to compare m-bit integers using GD. Need 2^m examples.

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Csaba Szepesvari @skiandsolve.bsky.social · 09/07/2025

Yeah, we cite this and this was a paper that got me started on this project!

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Csaba Szepesvari @skiandsolve.bsky.social · 08/07/2025

First position paper I ever wrote. "Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence" arxiv.org/abs/2506.23908 Background: I'd like LLMs to help me do math, but statistical learning seems inadequate to make this happen. What do you all think?

arxiv.org

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence

Sound deductive reasoning -- the ability to derive new knowledge from existing facts and rules -- is an indisputably desirable aspect of general intelligence. Despite the major advances of AI systems ...

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Csaba Szepesvari @skiandsolve.bsky.social · 06/05/2025

Our seminars are back. If you missed Max's talk, it is on YouTube and today I will host Jeongyeol from UWM who will talk about the curious case of why latent MDPs though scary at first sight might be tractable! Link to the seminar homepage: sites.google.com/view/rltheor...

sites.google.com

RL theory seminars

Welcome Looking for an online seminar that presents the latest advances in reinforcement learning theory? You just found it! We aim to bring you a virtual seminar (approximately) every Tuesday at 6pm ...

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Csaba Szepesvari @skiandsolve.bsky.social · 04/04/2025

Glad to see someone remembers these:)

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Csaba Szepesvari @skiandsolve.bsky.social · 20/03/2025

should be distinguished. The reason they should not is because they are indistinguishable. So at least those need to be collapsed. So yes, one can start with redundant models, where it will appear you could have epistemic uncertainty, but this is easy to rule out. 2/2

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Csaba Szepesvari @skiandsolve.bsky.social · 20/03/2025

I guess with a worst-case hat on, we just all die:) In other words, indeed, the distinction is useful inasmuch as the modelling assumptions are valid. And there the mixture of two Diracs over 0 and 1 actually is a bad example, because that says that two models that are identical as distributions 1/x

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Csaba Szepesvari @skiandsolve.bsky.social · 20/03/2025

Well, yes, to the degree that the model you use correctly reflects what's going on. Example with drug trials, randomized patient allocation. Result is effectiveness. Meaning of aleatoric and epistemic uncertainty should be clear and they help with explaining outcomes of the trial. 4/x

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Csaba Szepesvari @skiandsolve.bsky.social · 20/03/2025

One observes 1, there is epistemic uncertainty (the model could be the first or the second). Of course, nothing is black and white like this ever. And we talk about models here. Models are.. made up.. Usual blurb about usefulness of models. Should you care about this distinction? 3/x

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Csaba Szepesvari @skiandsolve.bsky.social · 20/03/2025

Epistemic uncertainty refers to whether given the data (and prior information), we can surely identify the data generating model. Example: Model class has two distributions; one has support {0,1}, the other has support {1}. One observes 0. There is no epistemic uncertainty. 2/X

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Csaba Szepesvari @skiandsolve.bsky.social · 20/03/2025

I don't get this: In the context of this terminology, data comes from a model. Aleatoric uncertainty refers to the case when this model is a Dirac! In the second case, the model is a mixture of two Dirac's. This is not a Dirac. Hence, there is aleatoric uncertainty. 1/X

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Reposted by Csaba Szepesvari

arXiv cs.LG Machine Learning @cslg-bot.bsky.social · 11/03/2025

Dylan J. Foster, Zakaria Mhammedi, Dhruv Rohatgi: Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration arxiv.org/abs/2503.07453 arxiv.org/pdf/2503.07453 arxiv.org/html/2503.07453

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Csaba Szepesvari @skiandsolve.bsky.social · 11/03/2025

But also we are how we act! So it's up to us all to behave so as to make statement true.

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Csaba Szepesvari @skiandsolve.bsky.social · 06/03/2025

www.youtube.com/watch?v=9_Pe... An interview with Rich. The humility of Rich is truly inspiring: "There are no authorities in science". I wish people would listen and live by this.

youtube.com

TURING AWARD WINNER Richard S. Sutton in Conversation with Cam Linke | No Authorities in Science

YouTube video by Amii

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Csaba Szepesvari @skiandsolve.bsky.social · 06/03/2025

That's all good: Bubbles join when they get up high into the blue sky:)

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Csaba Szepesvari @skiandsolve.bsky.social · 06/03/2025

LOL, the dude in the long white labcoat missed my poster at NeurIPS, how could he do that? LOL

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Csaba Szepesvari @skiandsolve.bsky.social · 23/02/2025

New journal that looks pretty good for your mathy type people caring about ML, AI and alike: mdlijournal.org

mdlijournal.org

Mathematics of Data, Learning, and Intelligence

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Reposted by Csaba Szepesvari

Ryan Williams @rrwilliams.bsky.social · 21/02/2025

New paper: Simulating Time With Square-Root Space people.csail.mit.edu/rrw/time-vs-... It's still hard for me to believe it myself, but I seem to have shown that TIME[t] is contained in SPACE[sqrt{t log t}]. To appear in STOC. Comments are very welcome!

people.csail.mit.edu

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Csaba Szepesvari @skiandsolve.bsky.social · 15/02/2025

Oh, it turns out you have to wait a little longer for *RLDM* to come to Edmonton! But!!! RLC will happen to be in Edmonton this year, so all is good, you RL folks are all covered!

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Csaba Szepesvari @skiandsolve.bsky.social · 12/02/2025

Yet another opportunity to visit Edmonton in the summer. If you come, ask me to take you on the river valley trails, or to go climbing together😄

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Csaba Szepesvari @skiandsolve.bsky.social · 12/02/2025

I also sense that you may be thinking that uniform error bounds are meant for error estimation. My sense is that they were meant for what they are, answering specific learnability questions. The literature on cross validation estimation is interesting though..

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Csaba Szepesvari @skiandsolve.bsky.social · 12/02/2025

Fine, I bite: Maybe because classical results sufficed (chapter 8 in the classic text of Devroye, Györfi and Lugosi) and there is nothing much else to add (except for the nasty cases like policy evaluation on batch data where there is no lack of engagement).

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Reposted by Csaba Szepesvari

Miro Dudik @mdudik.bsky.social · 16/01/2025

📣My team at Microsoft Research New York is hiring a senior researcher in AI, both broadly in AI/ML, and in some specific areas including science of deep learning and modular transfer learning. Apply by February 7, 2025 on the link below. jobs.careers.microsoft.com/global/en/jo...

jobs.careers.microsoft.com

Search Jobs | Microsoft Careers

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

..whether the interval does indeed contain the unknown parameter.

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

Maybe just say that the theoretical guarantees are concerned with how frequently the random effects will produce and incorrect interval and in particular they don't guarantee that the interval will be correct every time or that the user will be able to tell given the data 1/2

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

From the associated blog post: "The theoretical guarantees only hold before data is collected." I don't like sentences like this. Guarantees hold regardless of what we do. @beenwrekt.bsky.social, just realized this is your stuff. Am I just too grumpy today? I certainly appreciate the text otherwise

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

We will get disinterested. At least some part of the community. You don't do math just for the sake of knowing what's true or false but you do it for the sake of knowing why..

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

I'm my book that wouldn't be a randomizing algorithm. Classic stat texts talk about this randomization to get exact coverage but imho that's a bit of 'smoke in the eyes' thing.

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

Yes, it seems like a good idea to first consider the problem when you not only design the interval but you also want the procedure that tells you where the interval contains the unknown parameter. And then show this can only be solved in uninteresting ways. Can't turn uncertainty to certainty.

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

So they would want to have not only an interval but an indicator which with no exceptions tells you where the interval contains the data. And that you can't have that is hard to swallow. 2/2

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

I guess I get it know: I guess what the author means is that people are unhappy about that you never know whether your confidence interval on the data you use it with actually contains the true parameter or not. 1/2

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

Also, I wonder what could be a non ex ante guarantee for a problem like confidence interval design.. (wondering about the word "only" in the excerpt).

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

Specifically, randomization may appear to help with getting some tradeoff exactly right, but the problem is that randomization can be misused badly; the criteria based on expectations allows such misuse. And imho this is important.

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Csaba Szepesvari @skiandsolve.bsky.social · 13/01/2025

Maybe a better wording would have been: "even if we have no means of verifying success, ..." And I still don't know why we should be excited about randomization in interval design other than to address some weird optimality criteria related to lack of convexity.

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