A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control

28 Jan 2020 Jia-Jie Zhu Moritz Diehl Bernhard Schölkopf

We apply kernel mean embedding methods to sample-based stochastic optimization and control. Specifically, we use the reduced-set expansion method as a way to discard sampled scenarios... (read more)

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