# uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN

arXiv: https://arxiv.org/abs/2604.20255 · HTML: https://mlsanity.com/paper/2604.20255/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [On Diffusion Modeling for Anomaly Detection](https://mlsanity.com/paper/2305.18593/) (2023)
- [Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks](https://mlsanity.com/paper/2311.10609/) (2023)
- [Beyond Individual Input for Deep Anomaly Detection on Tabular Data](https://mlsanity.com/paper/2305.15121/) (2023)
- [Explainable Contextual Anomaly Detection using Quantile Regression Forests](https://mlsanity.com/paper/2302.11239/) (2023)
- [Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners](https://mlsanity.com/paper/2503.01256/) (2025)
- [Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data](https://mlsanity.com/paper/2508.00758/) (2025)
- [Factor Analysis of Mixed Data for Anomaly Detection](https://mlsanity.com/paper/2005.12129/) (2020)
- [Understanding the limitations of self-supervised learning for tabular anomaly detection](https://mlsanity.com/paper/2309.08374/) (2023)
- [Foundation Models for Anomaly Detection: Vision and Challenges](https://mlsanity.com/paper/2502.06911/) (2025)
- [LogDP: Combining Dependency and Proximity for Log-based Anomaly Detection](https://mlsanity.com/paper/2110.01927/) (2021)
- [Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation](https://mlsanity.com/paper/2011.06716/) (2020)
- [DAGnosis: Localized Identification of Data Inconsistencies using Structures](https://mlsanity.com/paper/2402.17599/) (2024)

## Built on by (most cited first, 1 of 1)

- [CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching](https://mlsanity.com/paper/2606.11473/) (2026)

## Abstract (arXiv)

Anomaly detection in tabular data is challenging due to high dimensionality, complex feature dependencies, and heterogeneous noise. Many existing methods rely on proximity-based cues and may miss anomalies caused by violations of complex feature dependencies. Dependency-based anomaly detection provides a principled alternative by identifying anomalies as violations of dependencies among features. However, existing methods often struggle to model such dependencies robustly and to scale to high-dimensional data with complex dependency structures. To address these challenges, we propose uLEAD-TabPFN, a dependency-based anomaly detection framework built on Prior-Data Fitted Networks (PFNs). uLEAD-TabPFN identifies anomalies as violations of conditional dependencies in a learned latent space, leveraging frozen PFNs for dependency estimation. Combined with uncertainty-aware scoring, the proposed framework enables robust and scalable anomaly detection. Experiments on 57 tabular datasets from ADBench show that uLEAD-TabPFN achieves particularly strong performance in medium- and high-dimensional settings, where it attains the top average rank. On high-dimensional datasets, uLEAD-TabPFN improves the average ROC-AUC by nearly 20% over the average baseline and by approximately 2.8% over the best-performing baseline, while maintaining overall superior performance compared to state-of-the-art methods. Further analysis shows that uLEAD-TabPFN provides complementary anomaly detection capability, achieving strong performance on datasets where many existing methods struggle.