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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
6883
Venue
SIGMOD
Year
2024
Pagerank
4.9972581e-05
Overall Rank
6,600 | 54.09%
DOI
10.1145/3654920

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3,311 Efficient and Effective Data Imputation with Influence Functions 2022 VLDB 7.2406486e-05
4,434 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.1929999e-05
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