Gilles Louppe
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glouppe.bsky.social
Gilles Louppe
@glouppe.bsky.social
3.7K followers 840 following 67 posts
AI for Science, deep generative models, inverse problems. Professor of AI and deep learning @universitedeliege.bsky.social. Previously @CERN, @nyuniversity. https://glouppe.github.io
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glouppe.bsky.social
It is only useful when the training data is noisy or incomplete. See eg. arxiv.org/abs/2405.13712 where train diffusion models from sparse images only.
Learning Diffusion Priors from Observations by Expectation Maximization
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove diffi...
arxiv.org
October 27, 2025 at 3:38 PM
glouppe.bsky.social
Bravo Gaël pour cette reconnaissance amplement méritée! Tu dis que la science est un sport d'équipe, et c'est vrai, mais une équipe a besoin d'un leader qui l'inspire. Je suis sûr que l'équipe scikit-learn, passée et présente, est fière de toi et de ce que nous avons construit ensemble. Champagne!
October 10, 2025 at 12:35 PM
Reposted by Gilles Louppe
gaelvaroquaux.bsky.social
Yeah, I think that "AI is the new petrol" is about right...
October 1, 2025 at 5:51 AM
Reposted by Gilles Louppe
mraginsky.bsky.social
Rudolf Kalman put it nicely (and provocatively): link.springer.com/chapter/10.1...
Quote from R. Kalman, "Identification from real data": For an objective outsider, much of the historical development of statistics is a long series of attempts to dodge the inevitable implications of uncertainty. Whenever the conventional statistical treatment of a problem gives a unique (certain) answer, as in maximum likelihood estimation, in least squares, ... , common sense should tell us that such a miracle is possible only if additional assumptions (deus ex machina) are imposed on the data which somehow succeed in neutralizing the intrinsic uncertainty. We shall use the technical term "prejudice" for such assumptions. In other words, statistical methodology has been handicapped because statisticians have become mesmerized by the deep-seated hope of giving certain answers to problems where the uncertainty is intrinsic. This is politics, not science. For example, least squares is very popular because it always gives a unique answer. But this is exactly where its fatal weakness lies; when we pose Nature a question, as in identification problems, we must not phrase that question in such terms that the answer is predetermined regardless of the nature of the data.
September 27, 2025 at 5:42 PM