Hi, welcome to my homepage.
Previously, I was a research assistant at the Istituto Italiano di Tecnologia in the Computational Statistics and Machine Learning team in Genoa, working with Massimiliano Pontil and Carlo Ciliberto. From May 2020 to November 2020, I was a (remote) research intern with Pierre Alquier and Emtiyaz Khan in the Approximate Bayesian Inference Team of the RIKEN Center for Advanced Intelligence Project in Tokyo.
I graduated the Master MVA (Machine Learning and Computer Vision) from ENS Paris Saclay and obtained the engineering degree of ENSAE specialising in Statistics. Prior to that, I received a BSc in Mathematics from the Université Paris Dauphine and spent a semester at the University of Honk Kong.
- Z. Li, D. Meunier, M. Mollenhauer, A. Gretton, Optimal Rates for Regularized Conditional Mean Embedding Learning, 2022. Preprint arXiv:2208.01711.
- Distribution Regression with Sliced Wasserstein Kernels. Meunier, D.; Pontil, M.; Ciliberto, C. Proceedings of the 39th International Conference on Machine Learning (ICML), Proceedings of Machine Learning Research, 2022, vol. 162, pp. 15501–15523. Available on arXiv:2202.03926.
- Meta-strategy for Learning Tuning Parameters with Guarantees. Meunier, D.; Alquier, P. arXiv:2102.02504, Code. Entropy, 2021, vol. 23, no. 10, 1257. Part of the special issue on Approximate Bayesian Inference.
- Introduction to stochastic processes - Graduate (M1) - ENSAE Paris in Fall 2020 with Nicolas Chopin
- Tutor for first year students in Linear Algebra and Functional Analysis - Université Paris Dauphine in Fall 2017
- 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