Efficient Approximate Algorithms for Empirical Variance with Hashed Block Sampling-Reference-Cited by-同舟云学术

Efficient Approximate Algorithms for Empirical Variance with Hashed Block Sampling

Published:2022-08-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Chen Xingguang¹,Zhang Fangyuan¹,Wang Sibo¹

Affiliation:

1. The Chinese University of Hong Kong, Hong Kong, China

Funder

Hong Kong RGC CRF

NSFC of China

CUHK Direct Grant

Hong Kong RGC ECS

Hong Kong ITC ITF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539377

Reference43 articles.

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2. 2022. Managing Hash Clusters. https://docs.oracle.com/database/121/ADMIN/ hash.htm#ADMIN019. 2022. Managing Hash Clusters. https://docs.oracle.com/database/121/ADMIN/ hash.htm#ADMIN019.

3. 2022. Technical Report. https://github.com/xgchen8/Hash-BS. 2022. Technical Report. https://github.com/xgchen8/Hash-BS.

4. Noga Alon Phillip B. Gibbons Yossi Matias and Mario Szegedy. 1999. Tracking Join and Self-Join Sizes in Limited Storage. In PODS. 10--20. Noga Alon Phillip B. Gibbons Yossi Matias and Mario Szegedy. 1999. Tracking Join and Self-Join Sizes in Limited Storage. In PODS. 10--20.

5. Christopher M Bishop etal 1995. Neural networks for pattern recognition. Christopher M Bishop et al. 1995. Neural networks for pattern recognition.

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