Zikai Shen, Dimitri Meunier, Houssam Zenati, Arthur Gretton, Nathan Kallus, Aur\'elien Bibaut: Instrumental Variable Analysis Without Structural Equations https://arxiv.org/abs/2604.24660 https://arxiv.org/pdf/2604.24660 https://arxiv.org/html/2604.24660
Excited to launch Principia, a nonprofit research organisation at the intersection of deep learning theory and AI safety. Our goal is to develop theory for modern machine learning systems that can help us understand complex network behaviors, including those critical for AI safety and alignment. 1
At #NeurIPS ? Visit our posters! 🧵 Demystifying Spectral Feature Learning for Instrumental Variable Regression: #2600, Wed 11am Regularized least squares learning with heavy-tailed noise is minimax optimal: #3012, Wed 4:30pm ✨spotlight✨ 1/2
Solenne Gaucher, la mathématicienne qui sort le genre de l’équation
Solenne Gaucher, la mathématicienne qui sort le genre de l’équation
« La Relève ». Chaque mois, « Le Monde Campus » rencontre un jeune qui bouscule les normes dans son domaine. A 31 ans, la docteure en mathématiques s’attaque aux biais algorithmiques de l’intelligence artificielle et a reçu en 2024 un prix pour ses travaux.
lemonde.fr
We've written a monograph on Gaussian processes and reproducing kernel methods (with @philipphennig.bsky.social, @sejdino.bsky.social and Bharath Sriperumbudur). arxiv.org/abs/2506.17366
Gaussian Processes and Reproducing Kernels: Connections and Equivalences
This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using reproducing kernel Hilb...
arxiv.org
Distributional Reduction paper with H. Van Assel, @ncourty.bsky.social, T. Vayer , C. Vincent-Cuaz, and @pfrossard.bsky.social is accepted at TMLR. We show that both dimensionality reduction and clustering can be seen as minimizing an optimal transport loss 🧵1/5. openreview.net/forum?id=cll...
Dimitri Meunier, Antoine Moulin, Jakub Wornbard, Vladimir R. Kostic, Arthur Gretton Demystifying Spectral Feature Learning for Instrumental Variable Regression https://arxiv.org/abs/2506.10899
Very much looking forward to this ! 🙌 Stellar line-up
Lénaïc Chizat@lenaicchizat.bsky.social · last yr.Announcing : The 2nd International Summer School on Mathematical Aspects of Data Science mathsdata2025.github.io EPFL, Sept 1–5, 2025 Speakers: Bach @bachfrancis.bsky.social Bandeira Mallat Montanari Peyré @gabrielpeyre.bsky.social For PhD students & early-career researchers Apply before May 15!
new preprint with the amazing @lviano.bsky.social and @neu-rips.bsky.social on offline imitation learning! learned a lot :) when the expert is hard to represent but the environment is simple, estimating a Q-value rather than the expert directly may be beneficial. lots of open questions left though!
🚨 New paper accepted at SIMODS! 🚨 “Nonlinear Meta-learning Can Guarantee Faster Rates” arxiv.org/abs/2307.10870 When does meta learning work? Spoiler: generalise to new tasks by overfitting on your training tasks! Here is why: 🧵👇
Nonlinear Meta-Learning Can Guarantee Faster Rates
Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target task. The main aim of ...
arxiv.org
Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms https://arxiv.org/abs/2405.14778
Mattes Mollenhauer, Nicole M\"ucke, Dimitri Meunier, Arthur Gretton: Regularized least squares learning with heavy-tailed noise is minimax optimal https://arxiv.org/abs/2505.14214 https://arxiv.org/pdf/2505.14214 https://arxiv.org/html/2505.14214
I have updated my slides on the maths of AI by an optimal pairing between AI and maths researchers ... speakerdeck.com/gpeyre/the-m...
I have cleaned a bit my lecture notes on Optimal Transport for Machine Learners arxiv.org/abs/2505.06589
Optimal Transport for Machine Learners
Optimal Transport is a foundational mathematical theory that connects optimization, partial differential equations, and probability. It offers a powerful framework for comparing probability distributi...
arxiv.org
Gabriel Peyr\'e Optimal Transport for Machine Learners https://arxiv.org/abs/2505.06589
New ICML 2025 paper: Nested expectations with kernel quadrature. We propose an algorithm to estimate nested expectations which provides orders of magnitude improvements in low-to-mid dimensional smooth nested expectations using kernel ridge regression/kernel quadrature. arxiv.org/abs/2502.18284
Great talk by Aapo Hyvärinen on non linear ICA at AISTATS 25’!
Density Ratio-based Proxy Causal Learning Without Density Ratios 🤔 at #AISTATS2025 An alternative bridge function for proxy causal learning with hidden confounders. arxiv.org/abs/2503.08371 Bozkurt, Deaner, @dimitrimeunier.bsky.social, Xu
Dinner in Siglap yesterday evening with the members of the ABI team & friends who are attending ICLR.
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression #ICLR25 openreview.net/forum?id=ReI... NNs ✨better than fixed-feature (kernel, sieve) when target has low spatial homogeneity, ✨more sample-efficient wrt Stage 1 Kim, @dimitrimeunier.bsky.social, Suzuki, Li
Our joint paper with Geoffrey Wolfer @gwolfer.bsky.social "Variance-Aware Estimation of the Kernel Mean Embedding" accepted for publication in the Journal of Machine Learning Research 🥳 arxiv.org/abs/2210.06672
Variance-Aware Estimation of Kernel Mean Embedding
An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded independently of the dimension of the space, prope...
arxiv.org
Juno Kim, Dimitri Meunier, Arthur Gretton, Taiji Suzuki, Zhu Li Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression https://arxiv.org/abs/2501.04898
