Conformal Prediction for Verifiable Learned Query Optimization

Summary: Cast LQO verification as a conformal prediction problem to yield user-controlled pre-execution latency bounds, CP-based runtime verification with violation handling, and adaptive CP to handle distribution shifts. Applied to Balsa/Lero/RTOS, CP-guided plan search uses those bounds to steer optimization, producing tight guarantees and large empirical gains. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: hb2f5910e4ee0069a
Venue: VLDB
Year: 2025
Pagerank: 5.2304457e-05
Overall Rank: 8,989 | 39.75%
DOI: 10.14778/3742728.3742755
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Hanwen Liu (University of Southern California)
2. Shashank Giridhara (Amazon)
3. Ibrahim Sabek (University of Southern California)

BibTeX Citation

@article{liu_vldb25,
        title = {{Conformal Prediction for Verifiable Learned Query Optimization}},
        author = {Liu, Hanwen and Giridhara, Shashank and Sabek, Ibrahim},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {8},
        pages = {2653--2666},
        doi = {10.14778/3742728.3742755},
        url = {https://doi.org/10.14778/3742728.3742755},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,420	Rethinking Query Optimization for Multi-Agent Systems	2027	VLDB	4.9605343e-05
10,946	ConRAD: Conformal Risk-Aware Neural Databases	2026	VLDB	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 18 of 18 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1	Access Path Selection in a Relational Database Management System	1979	SIGMOD	0.0023948467
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061463175
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035920657
142	Neo: A Learned Query Optimizer	2019	VLDB	0.00029114937
355	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00020146245
405	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00019020138
453	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.0001801482
508	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00017108973
545	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.0001657599
568	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD	0.00016221103
1,175	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	0.00011630532
1,740	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.7289523e-05
2,190	Lero: A Learning-to-Rank Query Optimizer	2023	VLDB	8.8848923e-05
2,523	Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation	2022	VLDB	8.3316032e-05
3,269	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB	7.463682e-05
4,202	Kepler: Robust Learning for Faster Parametric Query Optimization	2023	SIGMOD	6.7258607e-05
5,854	Modeling Shifting Workloads for Learned Database Systems	2024	SIGMOD	5.9520525e-05
9,236	BASE: Bridging the Gap between Cost and Latency for Query Optimization	2023	VLDB	5.1882723e-05

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