Data Augmentation for ML-driven Data Preparation and Integration
Summary: Tutorial on DA for ML-driven data preparation and integration in data management. Covers task-specific operators, interpolation, conditional generation, and policy learning; links to active learning and weak supervision. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuliang Li (Megagon Labs)
- 2. Xiaolan Wang (Megagon Labs)
- 3. Zhengjie Miao (Duke University)
- 4. Wang-Chiew Tan (Meta)
BibTeX Citation
@article{li_vldb21,
title = {{Data Augmentation for ML-driven Data Preparation and Integration}},
author = {Li, Yuliang and Wang, Xiaolan and Miao, Zhengjie and Tan, Wang-Chiew},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {3182--3185},
doi = {10.14778/3476311.3476403},
url = {https://doi.org/10.14778/3476311.3476403},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,860 | Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation | 2023 | SIGMOD | 5.3564029e-05 |
| 11,417 | Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet | 2023 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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