Associate Professor, INRIA - Paris, Sierra-project team
2 papers at NeurIPS 2025
We propose a method for safely learning controlled stochastic dynamics from trajectories by incrementally expanding an initial safe control set using kernel-based confidence bounds, with theoretical guarantees on both safety and estimation accuracy.
We frame dynamic regret minimization as a static regret problem in an RKHS
