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Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications
Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This survey synthesizes prediction interfaces, error measures, consistency--robustness trade-offs, and five representative construction mechanisms across online optimization, caching, learned data s
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