ConRAD: Conformal Risk-Aware Neural Databases

Summary: ConRAD embeds conformal risk control in neural graph databases, deriving per-operator thresholds with finite-sample, distribution-free recall guarantees. Quantile scalarization and a conformal gate improve precision while bypassing unnecessary neural inference. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID: h674875920c48fe5b
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,876 | 26.88%
DOI: 10.14778/3836663.3836705

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Sonia Horchidan (KTH Royal Institute of Technology)
2. Fabian Zeiher (KTH Royal Institute of Technology)
3. Xiangyu Shi (KTH Royal Institute of Technology)
4. Vasiliki Kalavri (Boston University)
5. Henrik Boström (KTH Royal Institute of Technology)
6. Ioannis Kontoyiannis (University of Cambridge)
7. Paris Carbone (KTH Royal Institute of Technology)

BibTeX Citation

@article{horchidan_vldb26,
        title = {{ConRAD: Conformal Risk-Aware Neural Databases}},
        author = {Horchidan, Sonia and Zeiher, Fabian and Shi, Xiangyu and Kalavri, Vasiliki and Boström, Henrik and Kontoyiannis, Ioannis and Carbone, Paris},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {11},
        pages = {3511--3524},
        doi = {10.14778/3836663.3836705},
        url = {https://doi.org/10.14778/3836663.3836705},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
9	Online Aggregation	1997	SIGMOD	0.00076195956
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046284649
51	Efficient Query Evaluation on Probabilistic Databases	2004	VLDB	0.00042936299
271	NoScope: Optimizing Neural Network Queries over Video at Scale	2017	VLDB	0.00022560564
362	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019989474
501	Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes	2024	VLDB	0.00017267905
541	BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics	2020	VLDB	0.00016657685
669	CAESURA: Language Models as Multi-Modal Query Planners	2024	CIDR	0.0001495987
748	Palimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing	2025	CIDR	0.00014281926
784	VerdictDB: Universalizing Approximate Query Processing	2018	SIGMOD	0.00014012614
1,042	YAGO3: A Knowledge Base from Multilingual Wikipedias	2015	CIDR	0.00012336872
2,182	Extending Relational Query Processing with ML Inference	2020	CIDR	8.8982998e-05
2,662	End-to-end Optimization of Machine Learning Prediction Queries	2022	SIGMOD	8.1596229e-05
2,982	Semantic Operators and Their Optimization: Enabling LLM-Based Data Processing with Accuracy Guarantees in LOTUS	2025	VLDB	7.7845174e-05
3,778	Optimizing Video Analytics with Declarative Model Relationships	2023	VLDB	7.0259119e-05
5,272	InferDB: In-Database Machine Learning Inference Using Indexes	2024	VLDB	6.2010954e-05
6,791	dbET: Execution Time Distribution-based Plan Selection	2023	SIGMOD	5.6811782e-05
9,954	Conformal Prediction for Verifiable Learned Query Optimization	2025	VLDB	5.1038322e-05
10,834	ConANN: Conformal Approximate Nearest Neighbor Search	2026	VLDB	4.9793485e-05

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