Material for “ Time - Varying Gaussian Process Bandit Optimization
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چکیده
t (x)2, as was to be shown. B Learning ✏ via Maximum-Likelihood In this section, we provide an overview of how ✏ can be learned from training data in a principled manner; the details can be found in [20, Section 4.3] and [6, Section 5]. Throughout this appendix, we assume that the kernel matrix is parametrized by a set of hyperparameters ✓ (e.g., ✓ = (⌫, l) for the Mátern kernel), and ✏. Let ȳ be a vector of observations such that the i-th entry is observed at time t
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تاریخ انتشار 2016