Distributionally robust optimization techniques in batch Bayesian optimization
We propose a novel, theoretically-grounded, acquisition function for batch Bayesian optimisation informed by insights from distributionally robust optimization. Our acquisition function is a lower bound on the well-known Expected Improvement function – which requires a multi-dimensional Gaussian Exp...
| Main Authors: | , , |
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| Format: | Journal article |
| Published: |
Cornell University
2017
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