Associate professor at Université Paris-Dauphine since 2015. JSPS fellow in the laboratory of Pr. Sugiyama (from 2014 to 2015) and visiting researcher, RIKEN AIP (summer 2017-).
Topics of interest
Trustworthy machine learning, Causal inference, interpretable AI
Project in Prairie
Florian Yger will address the questions of trust, explainability and interpretability in machine learning models (including deep learning) with a focus on the robustness to adversarial examples and counterfactual reasoning on data. This project has natural and practical applications in the medical field.
In the last decade, deep learning has made possible breakthrouhgts in several domains (e.g. computer vision, machine translation, games, …). Yet those hardly interpretable algorithms are fed with huge amounts of -sometimes sensitive- data and can suffer from malicious attacks: attacks on the privacy of the data and attacks on the robustness where adversarial examples are generated to fool the algorithm. This is a critical issue (especially in medical applications) and we feel that an effort toward a deeper theoretical analysis is needed.