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Data-driven Abstractions via Adaptive Refinements and a Kantorovich Metric.

, , , and . CDC, page 6038-6043. IEEE, (2023)

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Observational Robustness and Invariances in Reinforcement Learning via Lexicographic Objectives., , , , and . CoRR, (2022)Distributionally Robust Optimal and Safe Control of Stochastic Systems via Kernel Conditional Mean Embedding., , and . CDC, page 2016-2021. IEEE, (2023)Enhancing Data-Driven Stochastic Control via Bundled Interval MDP., , , , and . IEEE Control. Syst. Lett., (2024)Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty., , , and . AAAI, page 14701-14710. AAAI Press, (2023)Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions., , , and . FMAS@iFM, volume 395 of EPTCS, page 144-152. (2023)Inner Approximations of Stochastic Programs for Data-Driven Stochastic Barrier Function Design., , , , and . CDC, page 3073-3080. IEEE, (2023)Data-driven Abstractions via Adaptive Refinements and a Kantorovich Metric., , , and . CDC, page 6038-6043. IEEE, (2023)Data-driven memory-dependent abstractions of dynamical systems., , , and . L4DC, volume 211 of Proceedings of Machine Learning Research, page 891-902. PMLR, (2023)Data-driven Interval MDP for Robust Control Synthesis., , , , and . CoRR, (2024)Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics., , , and . QEST, volume 14287 of Lecture Notes in Computer Science, page 10-29. Springer, (2023)