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

Zhu, Jia-Jie; Diehl, Moritz; Schölkopf, Bernhard

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

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Zhu, Jia-Jie
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf, Bernhard
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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http://proceedings.mlr.press/v120/zhu20a.html
(Publisher version)

https://doi.org/10.48550/arXiv.2001.10398
(Preprint)

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Zhu, J.-J., Diehl, M., & Schölkopf, B. (2020). A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. In A. M. Bayen, A. Jadbabaie, G. Pappas, P. A. Parrilo, B. Recht, C. Tomlin, et al. (Eds.), Proceedings of the 2nd Conference on Learning for Dynamics and Control (pp. 915-923). PMLR. Retrieved from http://proceedings.mlr.press/v120/zhu20a.html.

Cite as: https://hdl.handle.net/21.11116/0000-000B-0A86-3

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