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We first construct a CP model and obtain user-controlled bounded ranges for the actual latency of LQO plans before execution. Then, we introduce CP-based runtime verification along with violation handling to ensure performance prior to execution. For both scenarios, we further extend our framework to handle distribution shifts in the dynamic environment using adaptive CP approaches. Finally, we present CP-guided plan search, which uses actual latency upper bounds from CP to heuristically guide query plan construction. We integrated our verification framework into three LQOs (Balsa, Lero, and RTOS) and conducted evaluations on several workloads. Experimental results demonstrate that our method is both accurate and efficient. Our CP-based approaches achieve tight upper bounds, reliably detect and handle violations. Adaptive CP maintains accurate confidence levels even in the presence of distribution shifts, and the CP-guided plan search improves both query plan quality (up to 9.84x) and planning time, with a reduction of up to 74.4% for a single query and 9.96% across all test queries from trained LQOs.<\/jats:p>","DOI":"10.14778\/3742728.3742755","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T13:32:53Z","timestamp":1756906373000},"page":"2653-2666","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Conformal Prediction for Verifiable Learned Query Optimization"],"prefix":"10.14778","volume":"18","author":[{"given":"Hanwen","family":"Liu","sequence":"first","affiliation":[{"name":"University of Southern California, Los Angeles, California, USA"}]},{"given":"Shashank","family":"Giridhara","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Palo Alto, California, USA"}]},{"given":"Ibrahim","family":"Sabek","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, California, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Angelopoulos and Stephen Bates","author":"Anastasios","year":"2022","unstructured":"Anastasios N. 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In Proceedings of the 1979 ACM SIGMOD international conference on Management of data. 23\u201334."},{"key":"e_1_2_1_46_1","unstructured":"Glenn Shafer and Vladimir Vovk. 2007. A Tutorial on Conformal Prediction. arXiv:0706.3188 [cs.LG] https:\/\/arxiv.org\/abs\/0706.3188"},{"key":"e_1_2_1_47_1","volume-title":"Wortman Vaughan (Eds.)","volume":"34","author":"Stankeviciute Kamile","year":"2021","unstructured":"Kamile Stankeviciute, Ahmed M. Alaa, and Mihaela van der Schaar. 2021. Conformal Time-series Forecasting. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 6216\u20136228. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/312f1ba2a72318edaaa995a67835fad5-Paper.pdf"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.14778\/3368289.3368296"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.14778\/3485450.3485459"},{"key":"e_1_2_1_50_1","volume-title":"Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. arXiv preprint arXiv:1503.00075","author":"Tai Kai Sheng","year":"2015","unstructured":"Kai Sheng Tai, Richard Socher, and Christopher D Manning. 2015. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. arXiv preprint arXiv:1503.00075 (2015)."},{"key":"e_1_2_1_51_1","unstructured":"M. F. Taufiq J.-F. Ton R. Cornish et al. 2022. Conformal Off-Policy Prediction in Contextual Bandits. arXiv preprint arXiv:2206.04405 (2022)."},{"key":"e_1_2_1_52_1","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc.","author":"Tibshirani Ryan J.","year":"2019","unstructured":"Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Cand\u00e8s, et al. 2019. Conformal Prediction under Covariate Shift. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 227, 11 pages."},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications (Proceedings of Machine Learning Research","volume":"153","author":"Volkhonskiy Denis","year":"2017","unstructured":"Denis Volkhonskiy, Evgeny Burnaev, Ilia Nouretdinov, et al. 2017. Inductive Conformal Martingales for Change-Point Detection. In Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications (Proceedings of Machine Learning Research, Vol. 60), Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos (Eds.). PMLR, 132\u2013153. https:\/\/proceedings.mlr.press\/v60\/volkhonskiy17a.html"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1214\/20-sts817"},{"volume-title":"Algorithmic Learning in a Random World","author":"Vovk Vladimir","key":"e_1_2_1_55_1","unstructured":"Vladimir Vovk, Alex Gammerman, and Glenn Shafer. 2005. Algorithmic Learning in a Random World. Springer-Verlag, Berlin, Heidelberg."},{"volume-title":"Faulty Systems. In Proceedings of the First International Conference on Runtime Verification (St. Julians, Malta) (RV'10)","author":"Cristina","key":"e_1_2_1_56_1","unstructured":"Cristina M. Wilcox and Brian C. Williams. 2010. Runtime Verification of Stochastic, Faulty Systems. In Proceedings of the First International Conference on Runtime Verification (St. Julians, Malta) (RV'10). Springer-Verlag, Berlin, Heidelberg, 452\u2013459."},{"key":"e_1_2_1_57_1","first-page":"10","volume-title":"Proc. ACM Manag. Data 2, 1, Article 38 (March","author":"Wu Peizhi","year":"2024","unstructured":"Peizhi Wu and Zachary G. Ives. 2024. Modeling Shifting Workloads for Learned Database Systems. Proc. ACM Manag. Data 2, 1, Article 38 (March 2024), 27 pages. 10.1145\/3639293"},{"key":"e_1_2_1_58_1","volume-title":"Ives","author":"Wu Peizhi","year":"2023","unstructured":"Peizhi Wu, Ryan Marcus, and Zachary G. Ives. 2023. Adding Domain Knowledge to Query-Driven Learned Databases. arXiv:2312.01025 [cs.DB] https:\/\/arxiv.org\/abs\/2312.01025"},{"key":"e_1_2_1_59_1","volume-title":"Balsa: Learning a Query Optimizer Without Expert Demonstrations. In SIGMOD.","author":"Z. Yang","year":"2022","unstructured":"Z. Yang et al. 2022. 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