GradPAO: Gradient-Based Pseudo-Anomaly Optimization for Tabular Anomaly Detection

Tabular anomaly detection (TAD) is an important task since many real-world machine learning applications operate on tabular data, and it remains challenging due to the scarcity of labeled samples and the ambiguous definition of anomalies. Despite the rapid progress of tabular foundation models (TFMs) on predictive tasks, building a foundation model for TAD is especially difficult since its anomaly prior must cover diverse data types and define what counts as abnormal before any target dataset is seen. We therefore ask how a pretrained TFM can be turned into an anomaly detector, rather than developing another foundation model for TAD. The obstacle is that in-context learning of TFMs operates on a labeled context set, while only normal samples are available in TAD, so no anomaly class exists for a test query to be assigned to. To fill this gap, we propose **Grad**ient-Based **P**seudo **A**nomaly **O**ptimization for tabular anomaly detection (**GradPAO**), a novel framework that supplies the missing classes by defining pseudo-anomalies as trainable parameters and optimizing them through the foundation model. **GradPAO** first places the pseudo-anomalies using the inter-sample attention of the foundation model, and then optimizes them to be separable from the normal samples and diverse among themselves. Extensive experiments on two benchmarks with 57 and 690 datasets demonstrate that **GradPAO** achieves state-of-the-art performance.