Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix
Summary: ASCS: online, one-pass sketching for sparse, trillion-scale covariance estimation. It introduces an active sampling strategy that boosts SNR versus vanilla Count Sketch, enabling accurate recovery of covariance entries in high-dimensional, sparse data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhenwei Dai (Rice University)
- 2. Aditya Desai (Rice University)
- 3. Reinhard Heckel (Technical University of Munich)
- 4. Anshumali Shrivastava (Rice University)
BibTeX Citation
@inproceedings{dai_sigmod21,
title = {{Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix}},
author = {Dai, Zhenwei and Desai, Aditya and Heckel, Reinhard and Shrivastava, Anshumali},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457327},
url = {https://dl.acm.org/doi/10.1145/3448016.3457327},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,498 | Stingy Sketch: A Sketch Framework for Accurate and Fast Frequency Estimation | 2022 | VLDB | 5.6031077e-05 |
| 7,716 | Double-Anonymous Sketch: Achieving Top-K-fairness for Finding Global Top-K Frequent Items | 2023 | SIGMOD | 5.5605526e-05 |
| 8,851 | Memory-Efficient and Flexible Detection of Heavy Hitters in High-Speed Networks | 2023 | SIGMOD | 5.357707e-05 |
| 10,295 | Sublime: Sublinear Error & Space for Unbounded Skewed Streams | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,294 | Augmented Sketch: Faster and More Accurate Stream Processing | 2016 | SIGMOD | 0.00011291308 |
| 1,905 | Cold Filter: A Meta-Framework for Faster and More Accurate Stream Processing | 2018 | SIGMOD | 9.5034849e-05 |
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