Short bio
Florian Yger is an associate professor of computer science at Université Paris Dauphine-PSL and a researcher in the MILES team at LAMSADE since 2015. From 2014 to 2015, he was a JSPS postdoctoral fellow in the laboratory of Pr. Sugiyama in Tokyo University. He received his PhD in Computer science from LITIS, Université de Rouen under the supervision of Alain Rakotomamonjy in 2013. He is a visiting researcher at RIKEN AIP, Japan since 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.
Quote
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.
Team
