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: 5.1038322e-05
Overall Rank: 9,954 | 33.08%
DOI: 10.14778/3742728.3742755

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 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,876	ConRAD: Conformal Risk-Aware Neural Databases	2026	VLDB	4.9793485e-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	0.0023947656
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061066921
85	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035864347
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
362	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019989474
406	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00019045544
461	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.00017829982
512	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00017050173
560	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.00016403151
569	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD	0.00016245271
1,199	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	0.00011563985
1,734	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.7545773e-05
2,210	Lero: A Learning-to-Rank Query Optimizer	2023	VLDB	8.8257742e-05
2,522	Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation	2022	VLDB	8.3477168e-05
3,327	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB	7.4207879e-05
4,202	Kepler: Robust Learning for Faster Parametric Query Optimization	2023	SIGMOD	6.7374091e-05
5,865	Modeling Shifting Workloads for Learned Database Systems	2024	SIGMOD	5.9659203e-05
9,276	BASE: Bridging the Gap between Cost and Latency for Query Optimization	2023	VLDB	5.204289e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	9,670	Low Rank Learning for Offline Query Optimization	2025	SIGMOD
2	569	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD
3	5,039	Leveraging Query Logs and Machine Learning for Parametric Query Optimization	2022	VLDB
4	5,656	Uncertainty Aware Query Execution Time Prediction	2014	VLDB
5	3,041	Configuration-Parametric Query Optimization for Physical Design Tuning	2008	SIGMOD
6	2,690	Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection	2022	VLDB
7	8,332	Learned Offline Query Planning via Bayesian Optimization	2025	SIGMOD
8	560	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB
9	11,283	Robust Plan Evaluation based on Approximate Probabilistic Machine Learning	2025	VLDB
10	6,308	Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective	2024	VLDB