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In online algorithms, learning-augmented algorithms refers to algorithms that use possibly wrong machine-learned predictions, judged by how well they do when predictions are good and how gracefully they degrade when they are bad. This meaning comes from Lykouris (2021) in Journal of the ACM.[1]
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