AWhat changed
Relates Gaussian-process upper-confidence bandit cumulative regret to maximal sampling information gain, using kernel spectral structure to derive sublinear-regret results under the source's assumptions.
Scientific scope
Relates Gaussian-process upper-confidence bandit cumulative regret to maximal sampling information gain, using kernel spectral structure to derive sublinear-regret results under the source's assumptions. This is the2009v1 GP-sampled/independent-Gaussian-noise/discretized-setting account, not a blanket import of later agnostic RKHS extensions or corrected constants. Kernel/domain/discretization and acquisition-optimization assumptions matter; finite runs need not find the exact global maximizer. Selected statements read, not an independent proof replay.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. This is the2009v1 GP-sampled/independent-Gaussian-noise/discretized-setting account, not a blanket import of later agnostic RKHS extensions or corrected constants. Kernel/domain/discretization and acquisition-optimization assumptions matter; finite runs need not find the exact global maximizer. Selected statements read, not an independent proof replay.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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