Bogunovic, Ilija;
Li, Zihan;
Krause, Andreas;
Scarlett, Jonathan;
(2022)
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits.
In: Koyejo, S and Mohamed, S and Agarwal, A and Belgrave, D and Cho, K and Oh, A, (eds.)
Advances In Neural Information Processing Systems 35 (NEURIPS 2022).
Neural Information Processing Systems (NIPS)
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Abstract
We consider the sequential optimization of an unknown, continuous, and expensive to evaluate reward function, from noisy and adversarially corrupted observed rewards. When the corruption attacks are subject to a suitable budget C and the function lives in a Reproducing Kernel Hilbert Space (RKHS), the problem can be posed as corrupted Gaussian process (GP) bandit optimization. We propose a novel robust elimination-type algorithm that runs in epochs, combines exploration with infrequent switching to select a small subset of actions, and plays each action for multiple time instants. Our algorithm, Robust GP Phased Elimination (RGP-PE), successfully balances robustness to corruptions with exploration and exploitation such that its performance degrades minimally in the presence (or absence) of adversarial corruptions. When T is the number of samples and γT is the maximal information gain, the corruption-dependent term in our regret bound is O(CγT3/2), which is significantly tighter than the existing O(CpTγT) for several commonly-considered kernels. We perform the first empirical study of robustness in the corrupted GP bandit setting, and show that our algorithm is robust against a variety of adversarial attacks.
| Type: | Proceedings paper |
|---|---|
| Title: | A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits |
| Event: | 36th Conference on Neural Information Processing Systems (NeurIPS) |
| Location: | ELECTR NETWORK |
| Dates: | 28 Nov 2022 - 9 Dec 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| 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. |
| Keywords: | Computer Science, Computer Science, Artificial Intelligence, Computer Science, Information Systems, OPTIMIZATION, Science & Technology, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10198815 |
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