% Key papers for Anomaly detection with contaminated data (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2022_adbench_65622,
  title = {{ADBench: Anomaly Detection Benchmark}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55709},
}

@inproceedings{cvpr2022_catching_18444,
  title = {{Catching Both Gray and Black Swans: Open-Set Supervised Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2023_explicit_20939,
  title = {{Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22619},
}

@inproceedings{cvpr2023_diversity_20829,
  title = {{Diversity-Measurable Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21341},
}

@inproceedings{iclr2020_classification_29420,
  title = {{Classification-Based Anomaly Detection for General Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=H1lK_lBtvS},
}

@inproceedings{cvpr2024_anomaly_22772,
  title = {{Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30328},
}

@inproceedings{aaai2020_unsupervised_99978,
  title = {{Unsupervised Detection of Sub-Events in Large Scale Disasters}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5370},
}

@inproceedings{aaai2020_transfer_102206,
  title = {{Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6068},
}

@inproceedings{neurips2022_softpatch_66031,
  title = {{SoftPatch: Unsupervised Anomaly Detection with Noisy Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53707},
}

@inproceedings{aaai2019_learning_101736,
  title = {{Learning Competitive and Discriminative Reconstructions for Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4451},
}

@inproceedings{aaai2016_a_105002,
  title = {{A Framework for Outlier Description Using Constraint Programming}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10174},
}

@inproceedings{icml2022_latent_40748,
  title = {{Latent Outlier Exposure for Anomaly Detection with Contaminated Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17901},
}

@inproceedings{neurips2022_anoshift_66142,
  title = {{AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55623},
}

@inproceedings{aaai2022_transferring_2883,
  title = {{Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04128-transferring-the-contamination-factor-between-anomaly-detection-domains-by-shape-similarity/},
}

@inproceedings{icml2020_interpretable_38337,
  title = {{Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6171},
}

@inproceedings{neurips2024_unsupervised_70833,
  title = {{Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/98305},
}

@inproceedings{aaai2025_qsco_10412,
  title = {{Qsco: A Quantum Scoring Module for Open-Set Supervised Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34190},
}

@inproceedings{aaai2019_temporal_101567,
  title = {{Temporal Anomaly Detection: Calibrating the Surprise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4261},
}

@inproceedings{aaai2023_context_3314,
  title = {{Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26892},
}

@inproceedings{cvpr2025_anomalyncd_25489,
  title = {{AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32424},
}

@inproceedings{aaai2024_towards_7615,
  title = {{Towards Reproducible, Automated, and Scalable Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30303},
}

@inproceedings{neurips2024_unsupervised_70974,
  title = {{Unsupervised Anomaly Detection in The Presence of Missing Values}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96230},
}

@inproceedings{neurips2024_dissect_73356,
  title = {{Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94091},
}

@inproceedings{aaai2026_idk_106335,
  title = {{IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38642},
}

@inproceedings{neurips2025_out_76034,
  title = {{Out-of-Distribution Generalized Graph Anomaly Detection with Homophily-aware Environment Mixup}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=EpGtAOr4vs},
}

@inproceedings{neurips2025_an_76861,
  title = {{An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=NgLFQTBPRR},
}

@inproceedings{aaai2026_icad_106325,
  title = {{ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38632},
}

@inproceedings{aaai2024_graph_6148,
  title = {{Graph Anomaly Detection via Prototype-Aware Label Propagation (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30518},
}

@inproceedings{icml2024_online_43778,
  title = {{Online Isolation Forest}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34674},
}

@inproceedings{icml2025_towards_45921,
  title = {{Towards a Unified Framework of Clustering-based Anomaly Detection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46624},
}

@inproceedings{icml2026_mixture_50099,
  title = {{Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=91gJ3Zp2lT},
}

@inproceedings{icml2026_mechanistic_52131,
  title = {{Mechanistic Anomaly Detection via Functional Attribution}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=T1uBDl1WPR},
}

@inproceedings{icml2026_memory_53457,
  title = {{Memory-Distilled Selection for Noise-Robust Anomaly Detection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=fhXqiit0CW},
}

@inproceedings{neurips2025_fairness_78639,
  title = {{Fairness-aware Anomaly Detection via Fair Projection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=gjjsDmlQRW},
}

@inproceedings{neurips2025_autoscidact_80024,
  title = {{AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=vKyiv67VWa},
}

@inproceedings{neurips2025_anomaly_80028,
  title = {{Anomaly Detection by an Ensemble of Random Pairs of Hyperspheres}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=vM4PIjsJDG},
}