TabularARGN
Tabular Auto-Regressive Generative Network
Unlike synthetic data generators that rely on increasingly complex and resource-heavy architectures, TabularARGN adopts a more **focused and efficient model design**. These design choices result in:

* **High Fidelity**: TabularARGN achieves synthetic data quality on par with state-of-the-art (SOTA) models
* **Privacy by Design**: TabularARGN only considers privacy-preserving value ranges for sampling, and has built-in privacy protection features. Plus can be trained via DP-SGD for obtaining differential privacy guarantees.
* **Simplicity**: TabularARGN leverages existing building blocks, and thus can be easily implemented within standard deep learning frameworks.
* **Compute Efficiency**: With training speeds up to **100x faster**, TabularARGN scales effectively, even for large and complex datasets.
* **Sampling Flexibility**: TabularARGN supports advanced sampling capabilities, including:
   * **Conditional generation** to create targeted datasets.
   * **Missing value imputation** to handle incomplete data seamlessly.
   * **Fairness adjustments** to align with ethical data synthesis goals.
   * **Controlling sampling probabilities** via temperature adjustments to balance rule-adherence with data diversity.
* **Data Versatility**: TabularARGN accommodates the heterogeneity of real-world tabular datasets, including:
   * Multi-variate, mixed-type data (categorical, numerical, date-time, geo-spatial).
   * Multi-sequence datasets with varying sequence lengths and varying time intervals.
   * Missing values.
* **Robustness in Training**: TabularARGN delivers high-quality synthetic data with default settings and remains consistent across several training runs.