On Efficient Approximate Aggregate Nearest Neighbor Queries over Learned Representations
Summary: AQNN: aggregate statistics over the learned-representation neighborhood of a query object. Key idea is SPRinT, mixing high-quality but expensive embeddings with cheap ones via sampling + precision/recall-targeted NN selection, with error/sample-size bounds. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Carrie Wang (University of Hong Kong)
- 2. Sihem Amer-Yahia (CNRS; University of Grenoble)
- 3. Laks V. S. Lakshmanan (University of British Columbia)
- 4. Reynold Cheng (University of Hong Kong)
BibTeX Citation
@inproceedings{wang_sigmod26,
title = {{On Efficient Approximate Aggregate Nearest Neighbor Queries over Learned Representations}},
author = {Wang, Carrie and Amer-Yahia, Sihem and Lakshmanan, Laks V. S. and Cheng, Reynold},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786672},
url = {https://dl.acm.org/doi/10.1145/3786672},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 295 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00022238183 |
| 307 | Approximate Query Processing Using Wavelets | 2000 | VLDB | 0.00021792475 |
| 332 | Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search | 2016 | VLDB | 0.00020920444 |
| 705 | HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces | 2018 | VLDB | 0.00014829964 |
| 926 | Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination | 2020 | SIGMOD | 0.00013181732 |
| 2,898 | Approximate Selection with Guarantees using Proxies | 2020 | VLDB | 7.978725e-05 |
| 5,827 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 6.0744172e-05 |
| 9,353 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 5.2829539e-05 |
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