Nicolas Schreuder
About me
I am a research scientist ("Chargé de recherche") at CNRS (Laboratoire d'Informatique Gaspard-Monge).
Previously, I was a postdoctoral researcher working with Lorenzo Rosasco at Università di Genova, in Italy.
I hold a PhD in Statistics from CREST (Center for Research in Economics and Statistics) and Institut Polytechnique de Paris. My supervisors were Arnak Dalalyan and Victor-Emmanuel Brunel. My manuscript is available here.
I am mainly interested in statistical learning theory, high-dimensional statistics and fair learning.
I used to co-organize the Stat·Eco·ML bi-monthly seminar at CREST.
Preprints
- Antoine Chatalic, NS, Ernesto De Vito, Lorenzo Rosasco (2023). Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling. [arXiv:2311.13548]
- Ziyad Benomar, Evgenii Chzhen, NS, Vianney Perchet (2023). Addressing bias in online selection with limited budget of comparisons. [arXiv:2303.09205]
Publications
- Solenne Gaucher, NS, Evgenii Chzhen (2023). Fair learning with Wasserstein barycenters for non-decomposable performance measures. AISTATS 2023. [PMLR 206:2436-2459]
- Evgenii Chzhen, NS (2022). A minimax framework for quantifying risk-fairness trade-off in regression. Annals of Statistics. [10.1214/22-AOS2198]
- Antoine Chatalic, NS, Alessandro Rudi, Lorenzo Rosasco (2022). Nyström Kernel Mean Embeddings. ICML 2022. [PMLR 162:3006-3024]
- NS (2021). A study of some trade-offs in statistical learning: online learning, generative models and fairness. PhD manuscript. Institut Polytechnique de Paris. [tel-03435618]
- NS, Evgenii Chzhen (2021). Classification with abstention but without disparities. UAI 2021. Runner-up for best student paper award. [PMLR 161:1227-1236]
- NS, Victor-Emmanuel Brunel, Arnak Dalalyan (2021). Statistical guarantees for generative models without domination. ALT 2021. [PMLR 132:1051-1071]
- Evgenii Chzhen, NS (2020). An example of prediction which complies with Demographic Parity and equalizes group-wise risks in the context of regression. NeurIPS 2020 Workshop on Algorithmic Fairness through the Lens of Causality and Interpretability. [arXiv:2011.07158]
- NS (2020). Bounding the expectation of the supremum of empirical processes indexed by Hölder classes. Math. Meth. Stat. 29, 76–86 (2020). [link]
- NS, Victor-Emmanuel Brunel, Arnak Dalalyan (2019). A nonasymptotic law of iterated logarithm for general M-estimators. AISTATS 2020. [PMLR 108:1331-1341]
Education
- 2018 - 2021: PhD candidate, CREST, ENSAE Paris, Institut Polytechnique de Paris.
- 2017 - 2018: MVA Master degree, ENS Paris-Saclay.
- 2015 - 2018: Engineering Degree in Statistics, ENSAE Paris.
- 2012 - 2015: BSc in Applied Mathematics, Université Paris Dauphine-PSL.
Teaching (at ENSAE Paris)
2020-2021
- Advanced Machine Learning with V. Perchet.
- Theoretical Foundations of Machine Learning with V. Perchet.
- Measure Theory with A. Korba.
- Applied Statistics Project with J. Depersin.
2019-2020
- Numerical Analysis and Numerical Linear Algebra with S. M. Kaber.
- Applied Statistics Project with J. Depersin.