A Statistical Perspective on Discovering Functional Dependencies in Noisy Data

Author:

Zhang Yunjia1,Guo Zhihan1,Rekatsinas Theodoros1

Affiliation:

1. University of Wisconsin-Madison, Madison, WI, USA

Funder

DARPA

NSF

Publisher

ACM

Cited by 22 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. DAFDiscover: Robust Mining Algorithm for Dynamic Approximate Functional Dependencies on Dirty Data;Proceedings of the VLDB Endowment;2024-07

2. Rock: Cleaning Data by Embedding ML in Logic Rules;Companion of the 2024 International Conference on Management of Data;2024-06-09

3. Measuring Approximate Functional Dependencies: A Comparative Study;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

4. BClean: A Bayesian Data Cleaning System;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

5. Non-Invasive Fairness in Learning Through the Lens of Data Drift;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

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