Agree to Disagree: Robust Anomaly Detection with Noisy Labels
Summary: Unity: LNL for anomaly detection uniting sample selection and label refurbishment. Dual nets agree to pick clean labels, resolve disagreements for marginal cases, and apply anomaly-centric contrastive learning to refurbish the rest; iterative training yields strong F1 gains on 10 real datasets. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Dennis M. Hofmann (Worcester Polytechnic Institute)
- 2. Peter M. VanNostrand (Worcester Polytechnic Institute)
- 3. Lei Ma (Worcester Polytechnic Institute)
- 4. Huayi Zhang (ByteDance; Worcester Polytechnic Institute)
- 5. Joshua C. DeOliveira (Worcester Polytechnic Institute)
- 6. Lei Cao (Massachusetts Institute of Technology; University of Arizona)
- 7. Elke A. Rundensteiner (Worcester Polytechnic Institute)
BibTeX Citation
@inproceedings{hofmann_sigmod25,
title = {{Agree to Disagree: Robust Anomaly Detection with Noisy Labels}},
author = {Hofmann, Dennis M. and VanNostrand, Peter M. and Ma, Lei and Zhang, Huayi and DeOliveira, Joshua C. and Cao, Lei and Rundensteiner, Elke A.},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709657},
url = {https://dl.acm.org/doi/10.1145/3709657},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,792 | MacroBase: Prioritizing Attention in Fast Data | 2017 | SIGMOD | 9.7436856e-05 |
| 2,004 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 9.3207067e-05 |
| 3,376 | Robust and Transferable Log-based Anomaly Detection | 2023 | SIGMOD | 7.4605908e-05 |
| 4,062 | AutoOD: Automatic Outlier Detection | 2023 | SIGMOD | 6.9309994e-05 |
| 5,020 | Unsupervised Contextual Anomaly Detection for Database Systems | 2022 | SIGMOD | 6.3987603e-05 |
| 8,876 | LANCET: Labeling Complex Data at Scale | 2021 | VLDB | 5.3534114e-05 |
| 11,211 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB | 5.093636e-05 |
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