Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models
Summary: Selective data acquisition per slice to optimize accuracy and fairness; iterative learning-curve updates. Maintains per-slice learning curves and uses convex optimization to allocate data while handling inter-slice dependencies; validated on real crowdsourced data, outperforming baselines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,244 | Towards Benchmarking Feature Type Inference for AutoML Platforms | 2021 | SIGMOD | 5.6021738e-05 |
| 5,386 | Selective Data Acquisition in the Wild for Model Charging | 2022 | VLDB | 5.5346315e-05 |
| 5,982 | Responsible Data Integration: Next-generation Challenges | 2022 | SIGMOD | 5.2409386e-05 |
| 8,096 | Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications | 2023 | SIGMOD | 4.583522e-05 |
| 9,373 | Falcon: Fair Active Learning using Multi-armed Bandits | 2024 | VLDB | 4.3460825e-05 |
| 10,223 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 4.1905499e-05 |
| 10,488 | Data Enhancement for Binary Classification of Relational Data | 2025 | SIGMOD | 4.1905499e-05 |
| 10,564 | Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups | 2025 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 936 | Data Lake Management: Challenges and Opportunities | 2019 | VLDB | 0.00015197838 |
| 2,960 | The Role of Massively Multi-Task and Weak Supervision in Software 2.0 | 2019 | CIDR | 7.8103118e-05 |
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