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MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving
We find that our approach generates driving styles consistent with end-user styles (p<. 001) and participants rate our approach as more similar to their level of aggressiveness (p=. 002).
Meta-active Learning in Probabilistically-Safe Optimization
Learning to control a safety-critical system with latent dynamics (e. g. for deep brain stimulation) requires taking calculated risks to gain information as efficiently as possible.
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