ARGO: An Interactive Data Governance System for Machine Learning via Hierarchical Reinforcement Learning
Summary: ARGO treats ML data governance as adaptive sequential decision-making: hierarchical RL selects and localizes repairs, relabeling, augmentation, and removal. Human feedback guides policies and selectors through an interactive interface tracking data quality and model performance. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Shuang Hao (Beijing Jiaotong University)
- 2. Jun Wei (Beijing Jiaotong University)
- 3. Yingqi Mu (Beijing Jiaotong University)
- 4. Diwen Cheng (Beijing Jiaotong University)
BibTeX Citation
@article{hao_vldb26,
title = {{ARGO: An Interactive Data Governance System for Machine Learning via Hierarchical Reinforcement Learning}},
author = {Hao, Shuang and Wei, Jun and Mu, Yingqi and Cheng, Diwen},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4734--4737},
doi = {10.14778/3827998.3828109},
url = {https://doi.org/10.14778/3827998.3828109},
year = {2026}
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Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 483 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00017590977 |
| 3,617 | Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation | 2022 | SIGMOD | 7.1574349e-05 |
| 7,648 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB | 5.4772833e-05 |
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