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

AISTATS 2026 will be in Morocco!

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

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

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: 🧵👇

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

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

Link to the video: youtu.be/nLGBTMfTvr8?...

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

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