Reposted by Dimitri Meunier
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.
www.lemonde.fr
September 21, 2025 at 2:11 PM
Solenne Gaucher, la mathématicienne qui sort le genre de l’équation
Reposted by Dimitri Meunier
AISTATS 2026 will be in Morocco!
July 30, 2025 at 8:07 AM
AISTATS 2026 will be in Morocco!
Reposted by Dimitri Meunier
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
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
June 24, 2025 at 8:35 AM
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
arxiv.org/abs/2506.17366
Reposted by Dimitri Meunier
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...
June 27, 2025 at 7:44 AM
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...
Reposted by Dimitri Meunier
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
Demystifying Spectral Feature Learning for Instrumental Variable Regression
https://arxiv.org/abs/2506.10899
June 13, 2025 at 4:37 AM
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
Demystifying Spectral Feature Learning for Instrumental Variable Regression
https://arxiv.org/abs/2506.10899
Very much looking forward to this ! 🙌 Stellar line-up
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!
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!
Mathematical Aspects of Data Science
Graduate Summer School - EPFL - Sept. 1-5, 2025
mathsdata2025.github.io
May 29, 2025 at 2:41 PM
Very much looking forward to this ! 🙌 Stellar line-up
Reposted by Dimitri Meunier
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!
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!
May 27, 2025 at 7:13 AM
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!
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”
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
May 26, 2025 at 4:50 PM
🚨 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”
arxiv.org/abs/2307.10870
When does meta learning work? Spoiler: generalise to new tasks by overfitting on your training tasks!
Here is why:
🧵👇
Reposted by Dimitri Meunier
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
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
https://arxiv.org/abs/2405.14778
May 24, 2024 at 4:06 AM
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
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
https://arxiv.org/abs/2405.14778
Reposted by Dimitri Meunier
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
May 21, 2025 at 6:14 AM
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
Reposted by Dimitri Meunier
I have updated my slides on the maths of AI by an optimal pairing between AI and maths researchers ... speakerdeck.com/gpeyre/the-m...
May 20, 2025 at 11:21 AM
I have updated my slides on the maths of AI by an optimal pairing between AI and maths researchers ... speakerdeck.com/gpeyre/the-m...
Reposted by Dimitri Meunier
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
May 13, 2025 at 5:18 AM
I have cleaned a bit my lecture notes on Optimal Transport for Machine Learners arxiv.org/abs/2505.06589
Reposted by Dimitri Meunier
May 13, 2025 at 6:48 AM
Reposted by Dimitri Meunier
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
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
May 8, 2025 at 4:29 AM
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
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’!
May 4, 2025 at 2:57 AM
Great talk by Aapo Hyvärinen on non linear ICA at AISTATS 25’!
Reposted by Dimitri Meunier
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
at #AISTATS2025
An alternative bridge function for proxy causal learning with hidden confounders.
arxiv.org/abs/2503.08371
Bozkurt, Deaner, @dimitrimeunier.bsky.social, Xu
May 2, 2025 at 11:29 AM
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
at #AISTATS2025
An alternative bridge function for proxy causal learning with hidden confounders.
arxiv.org/abs/2503.08371
Bozkurt, Deaner, @dimitrimeunier.bsky.social, Xu
Reposted by Dimitri Meunier
Link to the video: youtu.be/nLGBTMfTvr8?...
Interview of Statistics and ML Expert - Pierre Alquier
YouTube video by ML New Papers
youtu.be
April 28, 2025 at 11:01 AM
Link to the video: youtu.be/nLGBTMfTvr8?...
Reposted by Dimitri Meunier
Dinner in Siglap yesterday evening with the members of the ABI team & friends who are attending ICLR.
April 27, 2025 at 9:41 AM
Dinner in Siglap yesterday evening with the members of the ABI team & friends who are attending ICLR.
Reposted by Dimitri Meunier
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
#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
April 22, 2025 at 10:23 PM
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
#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
Reposted by Dimitri Meunier
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
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
March 12, 2025 at 3:16 AM
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
arxiv.org/abs/2210.06672
Reposted by Dimitri Meunier
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
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
https://arxiv.org/abs/2501.04898
January 10, 2025 at 6:06 AM
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
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
https://arxiv.org/abs/2501.04898