Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem

03/31/2020

∙

by   Jia-Jie Zhu, et al.

∙

0

∙

share

In order to anticipate rare and impactful events, we propose to quantify the worst-case risk under distributional ambiguity using a recent development in kernel methods – the kernel mean embedding. Specifically, we formulate the generalized moment problem whose ambiguity set (i.e., the moment constraint) is described by constraints in the associated reproducing kernel Hilbert space in a nonparametric manner. We then present the tractable optimization formulation and its theoretical justification. As a concrete application, we numerically test the proposed method in characterizing the worst-case constraint violation probability in the context of a constrained stochastic control system.