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

@inproceedings{aaai2021_graph_683,
  title = {{Graph Neural Network-Based Anomaly Detection in Multivariate Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/04027-graph-neural-network-based-anomaly-detection-in-multivariate-time-series/},
}

@inproceedings{aaai2019_a_100591,
  title = {{A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/3942},
}

@inproceedings{aaai2023_detecting_3450,
  title = {{Detecting Multivariate Time Series Anomalies with Zero Known Label}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25623},
}

@inproceedings{neurips2023_memto_68282,
  title = {{MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71519},
}

@inproceedings{aaai2018_non_102365,
  title = {{Non-Parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11632},
}

@inproceedings{aaai2025_graph_9365,
  title = {{Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33921},
}

@inproceedings{aaai2025_gcad_9266,
  title = {{GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34096},
}

@inproceedings{neurips2023_drift_68644,
  title = {{Drift doesn't Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71195},
}

@inproceedings{neurips2023_nominality_69319,
  title = {{Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70582},
}

@inproceedings{aaai2025_tail_10893,
  title = {{TAIL-MIL: Time-Aware and Instance-Learnable Multiple Instance Learning for Multivariate Time Series Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33933},
}

@inproceedings{aaai2024_multi_6705,
  title = {{Multi-Scale Dynamic Graph Learning for Time Series Anomaly Detection (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30456},
}

@inproceedings{neurips2025_multivariate_80089,
  title = {{Multivariate Time Series Anomaly Detection with Idempotent Reconstruction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=w1x9WMMHvi},
}

@inproceedings{iclr2025_catch_83324,
  title = {{CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28489},
}

@inproceedings{aaai2026_candi_106217,
  title = {{CANDI: Curated Test-Time Adaptation for Multivariate Time-Series Anomaly Detection Under Distribution Shift}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38524},
}

@inproceedings{aaai2026_state_107418,
  title = {{State-Derivative-Aware Neural Controlled Differential Equations for Multivariate Time Series Anomaly Detection and Diagnosis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39770},
}

@inproceedings{neurips2025_physdiff_76026,
  title = {{PhysDiff: A Physically-Guided Diffusion Model for Multivariate Time Series Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ElTbpJp7b9},
}

@inproceedings{neurips2025_structured_77543,
  title = {{Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=V5kzCSeaXF},
}

@inproceedings{icml2025_causality_46609,
  title = {{Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45960},
}

@inproceedings{icml2026_cluster_51919,
  title = {{Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=R6JV2WOftz},
}

@inproceedings{icml2026_timelava_53501,
  title = {{TimeLAVA: Learning-Agnostic Valuation for Time Series Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=g5Id7eZRal},
}

@inproceedings{neurips2025_channel_77457,
  title = {{Channel Matters: Estimating Channel Influence for Multivariate Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=U2AF01VJyg},
}

@inproceedings{neurips2025_self_78684,
  title = {{Self-Perturbed Anomaly-Aware Graph Dynamics for Multivariate Time-Series Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=hJJnwcvE2M},
}

@inproceedings{neurips2025_fortifying_79071,
  title = {{Fortifying Time Series: DTW-Certified Robust Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=lG82mcrKJf},
}

@inproceedings{iclr2026_contextual_84423,
  title = {{Contextual and Seasonal LSTMs for Time Series Anomaly Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=2VtveTkmzW},
}

@inproceedings{iclr2026_low_87277,
  title = {{Low Rank Transformer for Multivariate Time Series Anomaly Detection and Localization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=ZtPIBpVojC},
}

@inproceedings{neurips2026_a_91151,
  title = {{A Locally Tokenized Generative Model for Robust Time-Series Watermarking}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150316},
}

@inproceedings{neurips2026_comet_94463,
  title = {{COMET: Codebook-based Online-adaptive Multi-scale Embedding for Time-series Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150690},
}