I am currently a Quentitative Researcher at CFM, where I mostly focus on ML techniques on the predictive side. More specifically, I work on text data and NLP techniques, as well as other data modalities such as audio, video and graphs.
Prior to that, I was a PhD student at Télécom ParisTech and Criteo. My PhD focused on Adversarial Robustness, in the context of Deep Learning and Rankings. You can find a nice introduction/tutorial on the subject here.
Previous work
Prior to joining CFM, I was a PhD student at Télécom ParisTech and Criteo. My PhD focused on Adversarial Robustness, in the context of Deep Learning and Rankings. You can find a nice introduction/tutorial on the subject here.
I also worked on:
- Stochastic processes. During an internship at the Univeristy Paris 1 - Panthéon-Sorbonne, I studied theoretical aspects of Lévy processes.
- Graph theory. I worked on compartmental epidemic models applied to the study of radicalization and terrorism at the University of Barcelona.
Publications
You can check my Google Scholar page.
My publications include:
2023
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Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues — Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stéphan Clémençon – ICML 2023
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Origins of Low-dimensional Adversarial Perturbations — Elvis Dohmatob, Chuan Guo, Morgane Goibert – AISTATS 2023
2022
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An Adversarial Robustness Perspective on the Topology of Neural Networks — Morgane Goibert, Thomas Ricatte, Elvis Dohmatob – ML Safety Workshop, NeurIPS 2022
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Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications — Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi – AISTATS 2022
2021
- Adversarial Robustness via Label-Smoothing — Morgane Goibert, Elvis Dohmatob – 2021