Paul Brunzema — accepted AI-conference papers

2025

BayeSQP: Bayesian Optimization through Sequential Quadratic Programming

NeurIPS 2025spotlight

We introduce BayeSQP, a novel algorithm for general black-box optimization that merges the structure of sequential quadratic programming with concepts from Bayesian optimization. BayeSQP employs second-order Gaussian process surrogates for both the objective and constraints to jointly model the func…

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2025

Bayesian Optimization via Continual Variational Last Layer Training

Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison

ICLR 2025spotlight

Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on tasks where correlations are easily captured (such as those defined by Euclidean metrics) and their ability to be effici…

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