Not surprised at neurips claiming there were no rejected papers based on space constraints when every AC knows first-hand that that's false. The eurips team flexing on them with the salon des refusés (although noble) surely didn't help; I wonder if it was discussed with the main organizers at all?

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!