AWhat changed
Optimized an approximate min-max neighborhood-loss objective by perturbing weights along a first-order direction and computing an update gradient at the perturbed weights.
Scientific scope
Optimized an approximate min-max neighborhood-loss objective by perturbing weights along a first-order direction and computing an update gradient at the perturbed weights. Inherits sharpness/generalization and robust-optimization ideas. Practical inner maximization and gradient omit higher-order terms; two gradient evaluations add cost. This does not globally solve the min-max problem or guarantee improved generalization on every task/reparameterization.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits sharpness/generalization and robust-optimization ideas. Practical inner maximization and gradient omit higher-order terms; two gradient evaluations add cost. This does not globally solve the min-max problem or guarantee improved generalization on every task/reparameterization.
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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