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arXiv Machine Learning By Long Vu, Jongseong Brad Choi, Binoy Ravindran, Zhoulai Fu

Cost-Aware Stopping for Freeze-Thaw Bayesian Optimization: When Does One-Step Lookahead Suffice?

Read the original on arXiv Machine Learning →

The paper introduces a cost‑aware stopping rule for Freeze‑Thaw Bayesian Optimization called EI stopping, which uses a one‑step lookahead to decide whether to continue evaluating a candidate. EI stopping compares the expected improvement of the next increment to its cost and stops when no improvement exceeds the cost, guaranteeing that it never performs worse than stopping immediately. The authors provide a theoretical certificate that identifies when EI stopping is premature and show through experiments that EI stopping performs comparably to a two‑step lookahead correction in high‑cost scenarios, while the correction outperforms EI stopping at low costs.

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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