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Missing Data Imputation with Uncertainty-Driven Network

Summary: NOMI: missing-data imputation via uncertainty-aware retrieval + neural-network Gaussian process imputator, explicitly targeting overfitting in deep distribution-modeling methods. Iterative calibration uses posterior uncertainty to refine local neighbor retrieval; EM interpretation gives the framework a neat theoretical footing. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
6945
Venue
SIGMOD
Year
2024
Pagerank
5.8157857e-05
Overall Rank
6,642 | 54.44%
DOI
10.1145/3654920

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod24,
        title = {{Missing Data Imputation with Uncertainty-Driven Network}},
        author = {Wang, Jianwei and Zhang, Ying and Wang, Kai and Lin, Xuemin and Zhang, Wenjie},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654920},
        url = {https://dl.acm.org/doi/10.1145/3654920},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

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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
3,386 Efficient and Effective Data Imputation with Influence Functions 2022 VLDB 7.453827e-05
4,368 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.7393882e-05
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