Cluster of Excellence – Machine Learning for Science
Lifelong Reinforcement Learning
Dr. Claire Vernade is head of the Independent Research Group 'Lifelong Reinforcement Learning' at our Cluster of Excellence 'Machine Learning' at the University of Tübingen. In the 2022 DFG call on 'Artificial Intelligence Methods', she was awarded an Emmy Noether Independent Junior Research Group.
Research Interests
The Lifelong Reinforcement Learning (LRL) Lab studies interactive machine learning problems where feedback loops and long-term impact of actions must be taken into account to train agents. In particular, we want to build agents who anticipate changes in the environment and show adaptive and sample-efficient behaviours that improve with experience. A key step towards this goal is to understand the foundations of non-stationary reinforcement learning and related “consequential" machine learning.
Claire is a Group Leader at the University of Tuebingen, in the Cluster of Excellence Machine Learning for Science. She was awarded an Emmy Noether award under the DFG's AI Initiative call in 2022.
Her research is on sequential decision making. It mostly spans bandit problems, and theoretical Reinforcement Learning, but her research interests extend to Learning Theory and principled learning algorithms. While keeping in mind concrete problems, she focuses on theoretical approaches, aiming for provably optimal algorithms.
Previously, she was Research Scientist at DeepMind in London UK since November 2018 in the Foundations team lead by Prof. Csaba Szepesvari. She did a post-doc in 2018 with Prof. Alexandra Carpentier at the University of Magdeburg in Germany while working part-time as an Applied Scientist at Amazon in Berlin. She received her PhD from Telecom ParisTech in October 2017, under the guidance of Prof. Olivier Cappé.
Contact
Claire Vernade, Ph.D.
Lifelong Reinforcement Learning
Max-Planck-Institut for Intelligent Systems Max-Planck-Ring 4 Room No. 01.012 72076 Tübingen
