Maching learning techniques can produce controllers that provide great scalability and high performance. Classical control synthesis, on the other hand, ensures worst-case guarantees. While both approached have their advantages, openness and uncertanty of the real world may lead to catastrophic system failures. In this talk, I will present a way to balance the trade-off between performance and formal guarantees.


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