arXiv Machine Learning June 11, 2026 By Dayananda Herurkar, Federico Raue, Joachim Folz, J\"orn Hees, Andreas Dengel

TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data

Read the original on arXiv Machine Learning →

arXiv:2606. 11844v1 Announce Type: new Abstract: Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schemas, distribution shifts, and severe class imbalance.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios

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