Robust Bayesian Optimisation with Unbounded Corruptions

Abdelhamid Ezzerg; Ilija Bogunovic; Jeremias Knoblauch

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arXiv · 2511.15315

Robust Bayesian Optimisation with Unbounded Corruptions

Abdelhamid Ezzerg·Ilija Bogunovic·Jeremias Knoblauch

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Abstract

Bayesian Optimization is critically vulnerable to extreme outliers. Existing provably robust methods typically assume a bounded cumulative corruption budget, which makes them defenseless against even a single corruption of sufficient magnitude. To address this, we introduce a new adversary whose budget is only bounded in the frequency of corruptions, not in their magnitude. We then derive RCGP-UCB, an algorithm coupling the famous upper confidence bound (UCB) approach with a Robust Conjugate Gaussian Process (RCGP). We present stable and adaptive versions of RCGP-UCB, and prove that they achieve sublinear regret in the presence of up to $O(T^{1/4})$ and $O(T^{1/7})$ corruptions with possibly infinite magnitude. This robustness comes at near zero cost: without outliers, RCGP-UCB's regret bounds match those of the standard GP-UCB algorithm.

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Abdelhamid Ezzerg, Ilija Bogunovic, Jeremias Knoblauch. 2025-11-19. Robust Bayesian Optimisation with Unbounded Corruptions. https://arxiv.org/abs/2511.15315

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