Establishes learnability of signed-measure selectivity predictors with OOD generalization bounds and derives practical strategies that improve OOD accuracy and latency in query-driven models.
How good are query optimizers, really? Proceedings of the VLDB Endowment 9 , 3 (2015), 204–215
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A Practical Theory of Generalization in Selectivity Learning
Establishes learnability of signed-measure selectivity predictors with OOD generalization bounds and derives practical strategies that improve OOD accuracy and latency in query-driven models.