Unverified50% confidenceFactExact time
This study proposes that the existing cumulative regret guarantees of Bayesian optimization do not necessarily characterize the quality of the algorithm's final output.
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10/14/2026
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Related Claims
UnverifiedThe validity of the confidence sequences constructed by SLICE is independent of the accuracy of the scoring function; the quality of the score only affects the width of the interval (statistical efficiency) and does not compromise the coverage guarantee.77% similarUnverifiedWhen prior knowledge about possible fault patterns is available, selecting a sparse penalty structure that matches them often yields significant performance gains over general-purpose algorithms72% similarUnverifiedThe guarantees in the HOST paper are established under appropriate conditions, and the robustness of the algorithm to deviations from the Gaussian assumption still requires further exploration.72% similarUnverifiedInference optimization measures such as quantization and optimization may introduce subtle output differences while improving efficiency.72% similarUnverifiedThis parameterization reduces the entire estimation problem to a convex optimization problem, and convexity means there is no trouble from local optima, and the solution process is stable and reproducible.72% similar
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