Romain Tavenard

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Romain Tavenard

Romain Tavenard

@rtavenar

Prof. @UnivRennes_2. ML for time series. Maintainer of #tslearn package

Rennes, France Katılım Ağustos 2016
157 Takip Edilen370 Takipçiler
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Samuel Vaiter
Samuel Vaiter@vaiter·
📣 New preprint 📣 **Differentiable Generalized Sliced Wasserstein Plans** w/ L. Chapel @rtavenar We propose a Generalized Sliced Wasserstein method that provides an approximated transport plan and which admits a differentiable approximation. arxiv.org/abs/2505.22049 1/5
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Samuel Vaiter
Samuel Vaiter@vaiter·
I have multiple openings for M2 internship / PhD / postdoc in Nice (France) on topics related to bilevel optimization, automatic differentiation and safe machine learning. More details on my webpage samuelvaiter.com Contact me by email, and feel free to forward/RT :)
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Nicolas Keriven
Nicolas Keriven@n_keriven·
I have several internship/PhD positions on Graph ML available with ERC MALAGA 🤓 See details here: nkeriven.github.io/malaga/ Don't hesitate to contact me! (same if you are interested in a post-doc related to graph ML)
Nicolas Keriven@n_keriven

I'm thrilled to announce that my #ERCStG project has been accepted 🤓 **MALAGA: Reinventing the Theory of Machine Learning on Large Graphs** Many job openings coming up, see nkeriven.github.io/malaga for updates! Thank you @ERC_Research and all my collaborators past and future

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Samuel Vaiter
Samuel Vaiter@vaiter·
A Monge map, i.e., a solution to optimal transport Monge problems, may not always exist, be unique, or be symmetric with respect to the source and target distributions. It was one of the motivation to introduce Kantorovich relaxation. #page=115" target="_blank" rel="nofollow noopener">math.univ-lyon1.fr/~santambrogio/…
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Samuel Vaiter
Samuel Vaiter@vaiter·
Brenier's theorem states that the optimal transport map between two probability measures for quadratic cost is the gradient of a convex function. Moreover, it is uniquely defined up to a Lebesgue negligible set. ceremade.dauphine.fr/~carlier/Breni…
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Samuel Vaiter
Samuel Vaiter@vaiter·
Bilevel optimization problems with multiple inner solutions come typically in two flavors: optimistic and pessimistic. Optimistic assumes the inner problem selects the best solution for the outer objective, while pessimistic assumes the worst-case solution is chosen.
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Sibylle Marcotte
Sibylle Marcotte@SibylleMarcotte·
🏆Didn't get the Physics Nobel Prize this year, but really excited to share that I've been named one of the #FWIS2024 @FondationLOreal-@UNESCO French Young Talents alongside 34 amazing young researchers! This award recognizes my research on deep learning theory #WomenInScience 👩‍💻
Sibylle Marcotte tweet mediaSibylle Marcotte tweet media
École normale supérieure | PSL@ENS_ULM

#FWIS2024 🎖️@SibylleMarcotte, doctorante au département #mathématiques et applications de l'ENS @psl_univ, figure parmi les lauréates du Prix Jeunes Talents France 2024 @FondationLOreal @UNESCO #ForWomenInScience @AcadSciences @4womeninscience Félicitations à elle !!! 👏

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Björn Michele
Björn Michele@Bjoern_Michele·
🚨 Interested in domain adaptation and generalization for 3D data? 🚨 It’s tough to keep up with all the new amazing work. That’s why I’ve curated a comprehensive, easy-to-navigate list of publications. 📚 👉 Repo: bit.ly/DA_DG_3D
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Ievgen Redko
Ievgen Redko@IevgenRedko·
A simple, yet overlooked idea: LLMs with a finite vocabulary and context window are (finite) Markov chains :) An 🤩 internship of @oussamazekri_in collaboration with now officially our 1st year Ph.D. student @AmbroiseOdonnat @abenechehab @bleistein_linus & N. Boullé A 🧵⬇️
Oussama Zekri@oussamazekri_

🚀 Have you heard about @karpathy ‘s tweet on LLMs and Markov chains? Well, we build on this idea in our newest paper! 🤓 "𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 𝐚𝐬 𝐌𝐚𝐫𝐤𝐨𝐯 𝐂𝐡𝐚𝐢𝐧𝐬"   📑 arxiv.org/pdf/2410.02724… 1/🧵

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Stat.ML Papers
Stat.ML Papers@StatMLPapers·
Large Language Models as Markov Chains ift.tt/uPbQTkA
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Samuel Vaiter
Samuel Vaiter@vaiter·
Stein's Lemma states that for a normally distributed variable X, the expected value E[Xg(X)] = E[g’(X)] for any g absolutely continuous (derivative a.e.) such that E[|g’(X)|] < ∞. It is a central result for characterizing Gaussian data arxiv.org/abs/2104.08302
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Nicolas Keriven
Nicolas Keriven@n_keriven·
I'm thrilled to announce that my #ERCStG project has been accepted 🤓 **MALAGA: Reinventing the Theory of Machine Learning on Large Graphs** Many job openings coming up, see nkeriven.github.io/malaga for updates! Thank you @ERC_Research and all my collaborators past and future
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Gael Varoquaux 🦋
Gael Varoquaux 🦋@GaelVaroquaux·
Un évènement sur l'IA où je participais a été chamboulé par Extinction Rébellion (mauvaise cible: ça parlait des limites de l'IA). C'est symptomatique du contexte de communication contrôlée ne laissant aux voix dissonantes uniquement une place violente.
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