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PGTuner: An Efficient Framework for Automatic and Transferable Configuration Tuning of Proximity Graphs

Summary: PGTuner pre-trains a query-performance predictor to avoid costly proximity-graph rebuilds and uses deep reinforcement learning to recommend optimal construction/query parameters for target accuracy. It adds OOD detection and active learning for efficient transfer to new/dynamic datasets, yielding up to 14.7× speedups. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7526
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,326 | 29.16%
DOI
10.1145/3749179

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Authors

BibTeX Citation

@inproceedings{duan_sigmod26,
        title = {{PGTuner: An Efficient Framework for Automatic and Transferable Configuration Tuning of Proximity Graphs}},
        author = {DUAN, HAO and SONG, YITONG and YAO, BIN and LIANG, ANQI},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3749179},
        url = {https://dl.acm.org/doi/10.1145/3749179},
        year = {2026}
}

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Rank Citing Paper Year Venue Pagerank
10,201 Balancing Global and Local: Representative Sampling for Large-Scale Vector Data 2026 SIGMOD 5.093636e-05
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