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 (University of Toronto)
- 2. Nick Koudas (University of Toronto)
BibTeX Citation
@inproceedings{guan_sigmod22,
title = {{FILA: Online Auditing of Machine Learning Model Accuracy under Finite Labelling Budget}},
author = {Guan, Naiqing and Koudas, Nick},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517904},
url = {https://dl.acm.org/doi/10.1145/3514221.3517904},
year = {2022}
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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 |
|---|---|---|---|---|
| 134 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.00030043481 |
| 158 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028046388 |
| 244 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB | 0.00023314591 |
| 433 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018332741 |
| 2,323 | ZeroER: Entity Resolution using Zero Labeled Examples | 2020 | SIGMOD | 8.6348884e-05 |
| 2,475 | A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching | 2020 | SIGMOD | 8.410678e-05 |
| 3,706 | TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines | 2020 | SIGMOD | 7.0829596e-05 |
| 5,915 | In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling | 2017 | VLDB | 5.948779e-05 |
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