# Robust Variational Autoencoder for Tabular Data with Beta Divergence

arXiv: https://arxiv.org/abs/2006.08204 · HTML: https://mlsanity.com/paper/2006.08204/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 13 of 13)

- [Semi-supervised Learning using Robust Loss](https://mlsanity.com/paper/2203.01524/) (2022)
- [Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference](https://mlsanity.com/paper/2510.18768/) (2025)
- [C2VAE: Gaussian Copula-based VAE Differing Disentangled from Coupled Representations with Contrastive Posterior](https://mlsanity.com/paper/2309.13303/) (2023)
- [Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversampling](https://mlsanity.com/paper/2509.19032/) (2025)
- [Beta quantile regression for robust estimation of uncertainty in the presence of outliers](https://mlsanity.com/paper/2309.07374/) (2023)
- [MYCROFT: Towards Effective and Efficient External Data Augmentation](https://mlsanity.com/paper/2410.08432/) (2024)
- [Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework](https://mlsanity.com/paper/2604.20288/) (2026)
- [Scalable Microservice Forensics and Stability Assessment Using Variational Autoencoders](https://mlsanity.com/paper/2104.13193/) (2021)
- [Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints](https://mlsanity.com/paper/2505.05019/) (2025)
- [Synthetic Flight Data Generation Using Generative Models](https://mlsanity.com/paper/2604.20293/) (2026)
- [A Filtered Mixture-of-Generators for Fully Synthetic Survival Training](https://mlsanity.com/paper/2607.00127/) (2026)
- [Generating Benchmark Health Data Using a Tabular Diffusion Transformer](https://mlsanity.com/paper/2608.14496/) (2026)
- [Beyond Distributional Fidelity: Causal-Penalized Diffusion for Synthetic Tabular Data](https://mlsanity.com/paper/2610.11407/) (2026)

## Abstract (arXiv)

We propose a robust variational autoencoder with β divergence for tabular data (RTVAE) with mixed categorical and continuous features. Variational autoencoders (VAE) and their variations are popular frameworks for anomaly detection problems. The primary assumption is that we can learn representations for normal patterns via VAEs and any deviation from that can indicate anomalies. However, the training data itself can contain outliers. The source of outliers in training data include the data collection process itself (random noise) or a malicious attacker (data poisoning) who may target to degrade the performance of the machine learning model. In either case, these outliers can disproportionately affect the training process of VAEs and may lead to wrong conclusions about what the normal behavior is. In this work, we derive a novel form of a variational autoencoder for tabular data sets with categorical and continuous features that is robust to outliers in training data. Our results on the anomaly detection application for network traffic datasets demonstrate the effectiveness of our approach.