FILA: Online Auditing of Machine Learning Model Accuracy under Finite Labelling Budget
Summary: FILA: online auditing of ML model accuracy under finite labeling budget; sampling-based stratified estimator with human-in-the-loop. FILA-Thompson, Thompson-Sampling-driven variant, budgeted label allocation, asymptotic optimality, variance analysis. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Naiqing Guan
- 2. Nick Koudas
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Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 221 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.00033121824 |
| 300 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028441466 |
| 319 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB | 0.00027781866 |
| 643 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018754451 |
| 2,767 | A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching | 2020 | SIGMOD | 8.1513883e-05 |
| 3,140 | ZeroER: Entity Resolution using Zero Labeled Examples | 2020 | SIGMOD | 7.4841763e-05 |
| 3,491 | TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines | 2020 | SIGMOD | 7.0451276e-05 |
| 5,896 | In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling | 2017 | VLDB | 5.2847867e-05 |
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