High-Dimensional Gaussian Process Bandits

NeurIPS 2013 Josip DjolongaAndreas KrauseVolkan Cevher

Many applications in machine learning require optimizing unknown functions defined over a high-dimensional space from noisy samples that are expensive to obtain. We address this notoriously hard challenge, under the assumptions that the function varies only along some low-dimensional subspace and is smooth (i.e., it has a low norm in a Reproducible Kernel Hilbert Space)... (read more)

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