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Targeted Variance Reduction: Robust Bayesian Optimization of Black-Box Simulators with Noise Parameters
March 7, 2024, 5:42 a.m. | John Joshua Miller, Simon Mak
cs.LG updates on arXiv.org arxiv.org
Abstract: The optimization of a black-box simulator over control parameters $\mathbf{x}$ arises in a myriad of scientific applications. In such applications, the simulator often takes the form $f(\mathbf{x},\boldsymbol{\theta})$, where $\boldsymbol{\theta}$ are parameters that are uncertain in practice. Robust optimization aims to optimize the objective $\mathbb{E}[f(\mathbf{x},\boldsymbol{\Theta})]$, where $\boldsymbol{\Theta} \sim \mathcal{P}$ is a random variable that models uncertainty on $\boldsymbol{\theta}$. For this, existing black-box methods typically employ a two-stage approach for selecting the next point $(\mathbf{x},\boldsymbol{\theta})$, where $\mathbf{x}$ …
abstract applications arxiv bayesian box control cs.lg form noise optimization parameters practice robust stat.ml type uncertain variance
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