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Advancing Bayesian Optimization: The Mixed-Global-Local (MGL) Kernel and Length-Scale Cool Down | | Kim Peter Wabersich
; Marc Toussaint
; | | Date: |
9 Dec 2016 | | Abstract: | Bayesian Optimization (BO) has become a core method for solving expensive
black-box optimization problems. While much research focussed on the choice of
the acquisition function, we focus on online length-scale adaption and the
choice of kernel function. Instead of choosing hyperparameters in view of
maximum likelihood on past data, we propose to use the acquisition function to
decide on hyperparameter adaptation more robustly and in view of the future
optimization progress. Further, we propose a particular kernel function that
includes non-stationarity and local anisotropy and thereby implicitly
integrates the efficiency of local convex optimization with global Bayesian
optimization. Comparisons to state-of-the art BO methods underline the
efficiency of these mechanisms on global optimization benchmarks. | | Source: | arXiv, 1612.3117 | | Services: | Forum | Review | PDF | Favorites | |
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