3 citations · 4 across the 7 of their papers we have counts for
3 papers · 1 filter
Uncertainty Quantification via Invariant-Measure Conformal Prediction
Mohammadhossein Bakhtiaridoust, Dominik Baumann, Shankar Deka
Uncertainty quantification for learned stochastic dynamical systems is essential in safety-critical tasks such as control and monitoring. Standard conformal prediction provides fin…
Safe learning-based control via function-based uncertainty quantification
Abdullah Tokmak, Toni Karvonen, Thomas B. Schön +1
Uncertainty quantification is essential when deploying learning-based control methods in safety-critical systems. This is commonly realized by constructing uncertainty tubes that e…
Towards safe control parameter tuning in distributed multi-agent systems
Abdullah Tokmak, Thomas B. Schön, Dominik Baumann
Many safety-critical real-world problems, such as autonomous driving and collaborative robots, are of a distributed multi-agent nature. To optimize the performance of these systems…