[2603.19497] ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes

Computer Science > Machine Learning arXiv:2603.19497 (cs) [Submitted on 19 Mar 2026] Title:ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes Authors:Jack Yi Wei, Narges Armanfard View a PDF of the paper titled ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes, by Jack Yi Wei and 1 other authors View PDF HTML (experimental) Abstract:Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to anomaly-free training samples, fully unsupervised settings with unlabeled and potentially contaminated training data, and semi-supervised settings with limited anomaly labels. Existing deep learning approaches typically train dataset-specific models under the assumption of a single supervision regime, which limits their ability to leverage shared structures across anomaly detection tasks and to adapt to different supervision levels. We propose ICLAD, an in-context learning foundation model for tabular anomaly detection that generalizes across both datasets and supervision regimes. ICLAD is trained via meta-learning on synthetic tabular anomaly detection tasks, and at inference time, the model assigns anomaly scores by conditioning on the training set without updating model weights. Comprehensive experiments on 57 tabular datasets from ADBench show that our method achieves state-of-the-art performance across three supervision re...