I heard that it's easier to get an h100 on Jean Zay than an a100, kind of funny. The hour multiplier for consumption (i.e. one h100 hour costs 4 credits) should take into account demand.

Come check out our #ICCV2025 poster for "Multi-modal Identity Extraction" at (Exhibit Hall I #73).

www.arxiv.org/abs/2508.09628 more evidence that frank-wolfe is all you need

you can improve your collaborators' writing clarity by being too dumb to fill in the gaps of what they've written, and arguing it must be wrong until they write it clearly enough that even you can understand.

I started to read this paper arxiv.org/abs/2510.17503 and I thought huh the analysis is so much like Frank-Wolfe, then I remembered that Frank-Wolfe and DC algorithms are dual. Probably, a Frank-Wolfe god like Jaggi knows that but it's not mentioned in the paper; I must be missing something simple.

Have you ever written a paper, and you see a small variation you could easily cover with your analysis etc but you don't do it? But you know if someone else did it right after, you would be upset you didn't include it? It happened to me again today! arxiv.org/abs/2510.16468

Abbas Khademi, Antonio Silveti-Falls Adaptive Conditional Gradient Descent https://arxiv.org/abs/2510.11440

Straight to the top of the "to read" list: arxiv.org/pdf/2510.09034

Now accepted at #NeurIPS2025 :)

In conditional gradient sliding you are using the conditional gradient algorithm to "chase" the projected Nesterov algorithm. Instead of computing the projection, you do some conditional gradient steps to approximate it. I wonder if you can do the same with FISTA/accelerated proximal point alg ?

nerd sniped by the bayesian learning rule again and still unsatisfied... ok, so you can explain a lot of DL optimization algorithms with certain approximations of various posteriors but that's kind of kicking the can down the road - the question becomes: why those approximations instead of others?

My paper on Generalized Gradient Norm Clipping & Non-Euclidean (L0, L1)-Smoothness (together with collaborators from EPFL) was accepted as an oral at NeurIPS! We extend the theory for our Scion algorithm to include gradient clipping. Read about it here arxiv.org/abs/2506.01913

Found this on r/math: priority dispute in pure math that has come to a head, arxiv.org/abs/2507.20816

My ANR JCJC Grant was funded! 🎉

The french branch of beyond meat missed the mark by not naming themselves beyond viande

🎉🎉🎉Our paper "Inexact subgradient methods for semialgebraic functions" is accepted at Mathematical Programming !! This is a joint work with Jerome Bolte, Eric Moulines and Edouard Pauwels where we study a subgradient method with errors for nonconvex nonsmooth functions. arxiv.org/pdf/2404.19517

mean-field this, mean-field that, how about a nice field for once

Doing analysis of stochastic Frank-Wolfe and steepest descent/generalized matching pursuit variants at the same time is useful. If your argument/setup isn't symmetric for both then something is probably wrong or you have formulated/parameterized things incorrectly.

Which lab is training a language model that can fix the latex for my beamer slides so that things don't shift a few pixels when I go to the next \onslide within a slide???

Our research group in the department of Mathematics and Computer Science at the University of Basel (Switzerland) is looking for several PhD candidates and one post-doc who have a theoretical background in optimization and machine learning or practical experience in the field of reasoning.

Really not a fan of people's "creative" paper titles. A few people are able to do it well/tastefully but it inspires so many bad/cringe titles and it's worse for keyword searching.

That problem is smooth. And if it's not, it is differentiable everywhere. And if it's not, we avoid the kinks almost surely. And if we don't, what is computed is a subgradient. And if it's not, it approximates one. And if that's not true, who cares? The loss went down.

Tarski—Seidenberg theorem claims that semialgebraic sets on 𝐑 are stable by projection. perso.univ-rennes1.fr/michel.coste...

We also provide the first convergence rate analysis that I'm aware of for stochastic unconstrained Frank-Wolfe (i.e., without weight decay), which directly covers the muon optimizer (and much more)!

Anne Gagneux, Ségolène Martin, @quentinbertrand.bsky.social Remi Emonet and I wrote a tutorial blog post on flow matching: dl.heeere.com/conditional-... with lots of illustrations and intuition! We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423

New blog post: the Hutchinson trace estimator, or how to evaluate divergence/Jacobian trace cheaply. Fundamental for Continuous Normalizing Flows mathurinm.github.io/hutchinson/

If you have a PhD and you're interested in doing a postdoc near paris on nonsmooth frank Wolfe methods for machine learning, apply for an fmjh post doc here www.fondation-hadamard.fr/fr/programme... deadline is dec 9!