Name
Abstract | ||
|---|---|---|
Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds rang- ing og N from O(log N/root N) to O(e(-) (root N)), where N is the number of evaluations. This paper explores the possibility of improving the regret bound in the noiseless setting by intertwining concepts from BO and tree-based optimistic optimisation which are based on partitioning the search space. We propose the BOO algorithm, a first practical approach which can achieve an exponential regret bound with order O(N (- root N)) under the assumption that the objective function is sampled from a Gaussian process with a Matern kernel with smoothness parameter v > 4 + D/2, where D is the number of dimensions. We perform experiments on optimisation of various synthetic functions and machine learning hyperparameter tuning tasks and show that our algorithm outperforms baselines. |
Year | Venue | DocType |
|---|---|---|
2021 | INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 | Conference |
Volume | ISSN | Citations |
139 | 2640-3498 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hung Tran-The | 1 | 27 | 5.18 |
| Sunil Kumar Gupta | 2 | 238 | 41.55 |
| Santu Rana | 3 | 113 | 34.26 |
| Svetha Venkatesh | 4 | 4190 | 425.27 |