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- W2890055393 abstract "We develop an efficient and provably no-regret Bayesian optimization (BO) algorithm for optimization of black-box functions in high dimensions. We assume a generalized additive model with possibly overlapping variable groups. When the groups do not overlap, we are able to provide the first provably no-regret emph{polynomial time} (in the number of evaluations of the acquisition function) algorithm for solving high dimensional BO. To make the optimization efficient and feasible, we introduce a novel deterministic Fourier Features approximation based on numerical integration with detailed analysis for the squared exponential kernel. The error of this approximation decreases emph{exponentially} with the number of features, and allows for a precise approximation of both posterior mean and variance. In addition, the kernel matrix inversion improves in its complexity from cubic to essentially linear in the number of data points measured in basic arithmetic operations." @default.
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- W2890055393 date "2018-01-01" @default.
- W2890055393 modified "2023-09-24" @default.
- W2890055393 title "Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features" @default.
- W2890055393 doi "https://doi.org/10.3929/ethz-b-000309658" @default.
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