DemandClean: A Multi-Objective Learning Framework for Balancing Model Tolerance to Data Authenticity and Diversity
Summary: DemandClean: an RL framework that adaptively picks Repair/Delete/No to trade off data authenticity, feature diversity, and downstream models' noise tolerance. By leveraging error types (missing/semantic/syntactic) and interpretable visualizations, it matches or improves accuracy while cutting repair/deletion actions ≈80% vs Repair‑All, greatly reducing preprocessing cost. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Zekai Qian
- 2. Xiaoou Ding
- 3. Chen Wang
- 4. Hongzhi Wang
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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 |
|---|---|---|---|---|
| 192 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00035692958 |
| 3,397 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.1386386e-05 |
| 4,103 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD | 6.4460899e-05 |
| 9,558 | Clean4TSDB: A Data Cleaning Tool for Time Series Databases | 2024 | VLDB | 4.3212967e-05 |
| 9,560 | MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data | 2024 | VLDB | 4.3212967e-05 |
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| 11,553 | Active Reinforcement Learning for Data Preparation: Learn2Clean with Human-In-The-Loop | 2020 | CIDR | 4.1905499e-05 |
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