6 papers
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…
Safe Bayesian optimization across noise models via scenario programming
Abdullah Tokmak, Thomas B. Schön, Dominik Baumann
Safe Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample effic…
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…
Safe exploration in reproducing kernel Hilbert spaces
Abdullah Tokmak, Kiran G. Krishnan, Thomas B. Schön +1
Popular safe Bayesian optimization (BO) algorithms learn control policies for safety-critical systems in unknown environments. However, most algorithms make a smoothness assumption…
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations
Dominik Baumann, Erfaun Noorani, James Price +3
Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the re…