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arXiv Machine Learning By Colin Doumont, Henry Moss, Philipp Hennig

Fast Bayesian Optimization Through Linear Models

Read the original on arXiv Machine Learning →

The paper introduces fast Bayesian optimization (BO) methods that leverage linear structures in existing BO algorithms, enabling efficient optimization across a wide range of problem sizes—from 10 to over 6000 dimensions. These new methods achieve performance comparable to state‑of‑the‑art BO while being 100 to 1000 times faster, thereby expanding BO’s applicability to problems that were previously too time‑intensive. The authors also analyze the speed‑performance Pareto frontier, showing that many regions remain unfilled by current algorithms.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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