I am a PhD candidate at the Gatsby Computational Neuroscience Unit at UCL, working with Arthur Gretton. My research lies at the intersection of statistical learning theory and causal inference, with a particular focus on nonparametric instrumental variables, kernel methods, semiparametric inference, and representation learning.

During summer 2026, I am a visiting research scientist with Netflix’s Machine Learning and Inference Research (MLIR) team, working with Aurélien Bibaut and Nathan Kallus.

Before starting my PhD, I was a research assistant with the Computational Statistics and Machine Learning group at the Istituto Italiano di Tecnologia in Genoa, where I worked with Massimiliano Pontil and Carlo Ciliberto. In 2020, I was also a remote research intern with Pierre Alquier and Emtiyaz Khan in the Approximate Bayesian Inference team at the RIKEN Center for Advanced Intelligence Project in Tokyo.

Publications

Preprints

• Meunier D.*, Li Z.*, Christensen T., Gretton A., Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal. Available on arxiv:2411.19653. Submitted.

• Shen Z.*, Chen Z.*, Meunier D., Steinwart I., Li Z., Gretton A., Nonparametric Instrumental Variable Regression with Observed Covariates. Available on arxiv:2511.19404. Submitted.

• Bozkurt B., Galashov A., Meunier D., Shen Z., Gretton A., Zenati H., Doubly Robust Proxy Causal Learning with Neural Mean Embeddings. Available on arxiv:2605.09514. Submitted.

• Shen Z., Kallus N., Meunier D., Zenati H., Gretton A., Bibaut A., Instrumental Variable Analysis Without Structural Equations. Available on arxiv:2604.24660. Work in progress.

Journal

• Meunier D.*, Li Z.*, Gretton A., Kpotufe S., Nonlinear Meta-Learning Can Guarantee Faster Rates. SIAM Journal on Mathematics of Data Science, 2025, vol. 7, no. 4, pp. 1594-1615. Available on arxiv:2307.10870.

• Li Z.*, Meunier D.*, Mollenhauer M., Gretton A., Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. Journal of Machine Learning Research (JMLR), 2024, vol. 25, no. 181, pp. 1-51. Available on arxiv:2312.07186.

• Meunier D., Alquier P., Meta-strategy for Learning Tuning Parameters with Guarantees. Entropy, 2021, vol. 23, no. 10, 1257. Part of the special issue on Approximate Bayesian Inference. Available on arXiv:2102.02504.

Conference

• Wornbard J.*, Shen Z.*, Meunier D., Gretton A., Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity. Available on arxiv:2605.27526. To appear NeurIPS 2026.

• Meunier D.*, Wornbard J.*, Kostic V.*, Moulin A., Fröhlich A., Lounici K., Pontil M., Gretton A., Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression. Available on arxiv:2512.00919. To appear ICML 2026.

• Meunier D., Moulin A., Wornbard J., Kostic V., Gretton A., Demystifying Spectral Feature Learning for Instrumental Variable Regression. NeurIPS 2025. Available on arxiv:2506.10899.

• Mollenhauer M., Mücke N., Meunier D., Gretton A., Regularized least squares learning with heavy-tailed noise is minimax optimal. NeurIPS 2025 (Spotlight). Available on arxiv:2505.14214.

• Bozkurt B., Zenati H., Meunier D., Xu L., Gretton A., Density Ratio-Free Doubly Robust Proxy Causal Learning. NeurIPS 2025. Available on arxiv:2505.19807.

• Kim J., Meunier D., Gretton A., Suzuki T., Li Z., Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression. ICLR 2025. Available on arxiv:2501.04898.

• Bozkurt B., Deaner B., Meunier D., Xu L., Gretton A., Density Ratio-based Proxy Causal Learning Without Density Ratios. AISTATS 2025. Available on arxiv:2503.08371.

• Meunier D.*, Shen Z.*, Mollenhauer M., Gretton A., Li Z., Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. NeurIPS 2024. Available on arxiv:2405.14778.

• Li Z.*, Meunier D.*, Mollenhauer M., Gretton A., Optimal Rates for Regularized Conditional Mean Embedding Learning. NeurIPS 2022 (Oral). Available on arXiv:2208.01711.

• Meunier D., Pontil M., Ciliberto C., Distribution Regression with Sliced Wasserstein Kernels. ICML 2022. Available on arXiv:2202.03926.

* Denotes equal contribution.

Teaching

• Invited lecturer, Probabilistic AI School — three-hour lecture on kernel methods, Vilnius, August 2026
• Gatsby Bridging Programme - Linear Algebra - 2024 & 2025
• Advanced Topics in Machine Learning, Kernel Methods - Computational Statistics and Machine Learning MSc - UCL - Fall 2022 & 2023 with Arthur Gretton
• Introduction to stochastic processes - Graduate (M1) - ENSAE Paris - Fall 2020 with Nicolas Chopin
• Tutor for first year students in Linear Algebra and Functional Analysis - Université Paris Dauphine - Fall 2017

Education

• MSc in Statistics & Machine Learning, ENS Paris-Saclay, 2019-2020
• MSc in Statistics & Economics, ENSAE Paris, 2018-2020
• BSc in Mathematics, Université Paris Dauphine, 2014-2018

Reading groups

• Semiparametric statistics, Fall 2025 - Spring 2026
• PIMS online graduate course on Optimal Transport + Gradient Flows, Fall 2023
• Organiser of the Machine Learning Journal Club at Gatsby CNU, UCL, 2022-2023
• High-Dimensional Probability: An Introduction with Applications in Data Science, Roman Vershynin - January 2023 - March 2023
• Introductory Functional Analysis with Application, Erwin Kreyszig, June 2022 - December 2022
• Learning Theory from First Principles, Francis Bach, April 2021 - September 2021

Attendance

• ICML, Seoul, 2026
• Advances in Adaptive Experimentation Workshop, London, 2026
• Causality and machine learning, Isaac Newton Institute for Mathematical Sciences, Cambridge, 2026
• Causal machine learning for the social sciences, Isaac Newton Institute for Mathematical Sciences, Cambridge, 2026
• NeurIPS, San Diego, 2025
• Mathematical Aspects of Data Science Graduate Summer School, EPFL, Switzerland, 2025
• AISTATS, Phuket, 2025
• ICLR, Singapore, 2025
• 2nd RSS/Turing Workshop on Gradient Flows for Sampling, Inference, and Learning, The Alan Turing Institute, London, 2025
• NeurIPS, Vancouver, 2024
• Learning and Optimization in Luminy – LOL, CIRM, Luminy, 2024
• Workshop on Functional Inference and Machine Intelligence - FIMI, Bristol, 2024
• Machine Learning Summer School, OIST, Okinawa, 2024
• Gradient Flows For Sampling, Inference, and Learning, Royal Statistical Society, London, 2023
• Meeting in Mathematical Statistics, CIRM, Luminy, 2022
• NeurIPS, New Orleans, 2022
• ICML, Baltimore, 2022
• COLT, London, 2022