How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design. Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing. https://doi.org/10.1145/3406325.3451036
Chicago Style (17th ed.) Citation"How Much Data Is Sufficient to Learn High-performing Algorithms? Generalization Guarantees for Data-driven Algorithm Design." Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing . https://doi.org/10.1145/3406325.3451036.
MLA (9th ed.) Citation"How Much Data Is Sufficient to Learn High-performing Algorithms? Generalization Guarantees for Data-driven Algorithm Design." Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, , https://doi.org/10.1145/3406325.3451036.