# FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection

arXiv: https://arxiv.org/abs/2409.05672 · HTML: https://mlsanity.com/paper/2409.05672/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2024.

## Built on by (most cited first, 11 of 11)

- [Position: The Future of Bayesian Prediction Is Prior-Fitted](https://mlsanity.com/paper/2505.23947/) (2025)
- [From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection](https://mlsanity.com/paper/2602.03018/) (2026)
- [Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks](https://mlsanity.com/paper/2603.29475/) (2026)
- [ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes](https://mlsanity.com/paper/2603.19497/) (2026)
- [VIP-COP: Context Optimization for Tabular Foundation Models](https://mlsanity.com/paper/2605.12904/) (2026)
- [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://mlsanity.com/paper/2603.17570/) (2026)
- [Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning](https://mlsanity.com/paper/2605.07799/) (2026)
- [FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks](https://mlsanity.com/paper/2606.30336/) (2026)
- [When Identical Rows Disagree: From Benchmark Identifiability to Replication-Robust Anomaly Detection](https://mlsanity.com/paper/2609.29580/) (2026)
- [When Less Compute Is More: Adaptive Early Exit Improves Pretrained Outlier Detection](https://mlsanity.com/paper/2609.32898/) (2026)
- [TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks](https://mlsanity.com/paper/2609.36968/) (2026)

## Abstract (arXiv)

Outlier detection (OD) has a vast literature as it finds numerous real-world applications. Being an unsupervised task, model selection is a key bottleneck for OD without label supervision. Despite a long list of available OD algorithms with tunable hyperparameters, the lack of systematic approaches for unsupervised algorithm and hyperparameter selection limits their effective use in practice. In this paper, we present FoMo-0D, a pre-trained Foundation Model for zero/0-shot OD on tabular data, which bypasses the hurdle of model selection altogether. Having been pre-trained on synthetic data, FoMo-0D can directly predict the (outlier/inlier) label of test samples without parameter fine-tuning -- requiring no labeled data, and no additional training or hyperparameter tuning when given a new task. Extensive experiments on 57 real-world datasets against 26 baselines show that FoMo-0D is highly competitive; outperforming the majority of the baselines with no statistically significant difference from the 2nd best method. Further, FoMo-0D is efficient in inference time requiring only 7.7 ms per sample on average, with at least 7x speed-up compared to previous methods. To facilitate future research, our implementations for data synthesis and pre-training as well as model checkpoints are openly available at https://github.com/A-Chicharito-S/FoMo-0D.