Nicolas Schreuder
PhD student in Statistics
About me
I am a third year PhD student in Statistics at CREST (Center for Research in Economics and Statistics), (near) Paris, supervised by Arnak Dalalyan. I am mainly interested in statistical learning theory, high-dimensional statistics and fair learning. I co-organize the Stat·Eco·ML bi-monthly seminar at CREST.
Publications
- Nicolas Schreuder, Victor-Emmanuel Brunel, Arnak Dalalyan (2020). Statistical guarantees for generative models without domination. Submitted. [arXiv:2010.09237]
- Evgenii Chzhen, Nicolas Schreuder (2020). A minimax framework for quantifying risk-fairness trade-off in regression. Submitted. [arXiv:2007.14265]
- Nicolas Schreuder (2020). Bounding the expectation of the supremum of empirical processes indexed by Hölder classes. Submitted. [arXiv:2003.13530]
- Nicolas Schreuder, Victor-Emmanuel Brunel, Arnak Dalalyan (2019). A nonasymptotic law of iterated logarithm for general M-estimators. AISTATS 2020. [PMLR 108:1331-1341]
Events
- 11/12/2020: I will give a talk on "Quantifying risk-fairness trade-off in regression" at the NeurIPS 2020 Workshop on Fair AI in Finance.
- 12/12/2020: I will present a poster on "An example of prediction which complies with Demographic Parity and equalizes group-wise risks in the context of regression" at the NeurIPS 2020 Workshop on Algorithmic Fairness through the Lens of Causality and Interpretability.
- 20/12/2020: I will give a talk on "A minimax framework for quantifying risk-fairness trade-off in regression" at CMStatistics2020, Algorithmic fairness with statistical guarantees.
Education
- 2018 - 2021 (expected): PhD student, CREST.
- 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.
- 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.