Wilson, JT;
Hutter, F;
Deisenroth, MP;
(2018)
Maximizing acquisition functions for Bayesian optimization.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS 2018).
Neural Information Processing Systems (NIPS): Montreal, QC, Canada.
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Abstract
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes’ decision rule, but this ideal is difficult to achieve since these functions are frequently non-trivial to optimize. This statement is especially true when evaluating queries in parallel, where acquisition functions are routinely non-convex, highdimensional, and intractable. We first show that acquisition functions estimated via Monte Carlo integration are consistently amenable to gradient-based optimization. Subsequently, we identify a common family of acquisition functions, including EI and UCB, whose properties not only facilitate but justify use of greedy approaches for their maximization.
| Type: | Proceedings paper |
|---|---|
| Title: | Maximizing acquisition functions for Bayesian optimization |
| Event: | 32nd Conference on Neural Information Processing Systems (NIPS 2018), 3-8 December 2018, Montreal, QC, Canada |
| Location: | Montreal, CANADA |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://papers.nips.cc/paper/8194-maximizing-acqui... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10083559 |
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