Meta-Strategy for Learning Tuning Parameters with Guarantees. - Dimitri Meunier; Pierre Alquier (Entropy, 2021)

Distribution Regression with Sliced Wasserstein Kernels. - Dimitri Meunier; Massimiliano Pontil; Carlo Ciliberto (ICML, 2022)

Optimal Rates for Regularized Conditional Mean Embedding Learning. - Zhu Li; Dimitri Meunier; Mattes Mollenhauer; Arthur Gretton (NeurIPS, 2022)

Nonlinear Meta-Learning Can Guarantee Faster Rates. - Dimitri Meunier; Zhu Li; Arthur Gretton; Samory Kpotufe (CoRR, 2023)

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. - Dimitri Meunier; Zikai Shen; Mattes Mollenhauer; Arthur Gretton; Zhu Li (NeurIPS, 2024)

Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal. - Dimitri Meunier; Zhu Li; Tim Christensen; Arthur Gretton (CoRR, 2024)

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. - Zhu Li; Dimitri Meunier; Mattes Mollenhauer; Arthur Gretton (J. Mach. Learn. Res., 2024)

Density Ratio-based Proxy Causal Learning Without Density Ratios. - Bariscan Bozkurt; Ben Deaner; Dimitri Meunier; Liyuan Xu; Arthur Gretton (AISTATS, 2025)

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression. - Juno Kim; Dimitri Meunier; Arthur Gretton; Taiji Suzuki; Zhu Li (ICLR, 2025)

Regularized least squares learning with heavy-tailed noise is minimax optimal. - Mattes Mollenhauer; Nicole Mücke; Dimitri Meunier; Arthur Gretton (CoRR, 2025)

Density Ratio-Free Doubly Robust Proxy Causal Learning. - Bariscan Bozkurt; Houssam Zenati; Dimitri Meunier; Liyuan Xu; Arthur Gretton (CoRR, 2025)

Demystifying Spectral Feature Learning for Instrumental Variable Regression. - Dimitri Meunier; Antoine Moulin; Jakub Wornbard; Vladimir R. Kostic; Arthur Gretton (CoRR, 2025)