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Adaptive Data Augmentation for Supervised Learning over Missing Data

Summary: Adapts labeled source data to target-specific missingness rather than independently imputing both datasets. DAGAN uses coupled unsupervised GANs to learn target masks, augment source data while preserving labels, and retrain models for robust cross-pattern prediction. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12499
Venue
VLDB
Year
2021
Pagerank
6.486592e-05
Overall Rank
4,835 | 66.83%
DOI
10.14778/3450980.3450989

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{liu_vldb21,
        title = {{Adaptive Data Augmentation for Supervised Learning over Missing Data}},
        author = {Liu, Tongyu and Fan, Ju and Luo, Yinqing and Tang, Nan and Li, Guoliang and Du, Xiaoyong},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {7},
        pages = {1202--1214},
        doi = {10.14778/3450980.3450989},
        url = {https://doi.org/10.14778/3450980.3450989},
        year = {2021}
}

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