A supervised generative optimization approach for tabular data
This work addresses the problem of synthetic data generation for financial institutions, offering a task-aware approach that is incremental over existing unsupervised methods.
The paper tackles the challenge of selecting and optimizing synthetic data generation methods for specific downstream tasks by introducing a supervised generative optimization framework that integrates task-specific supervision and meta-learning to learn optimal mixture distributions, achieving improved performance on financial datasets.
Synthetic data generation has emerged as a crucial topic for financial institutions, driven by multiple factors, such as privacy protection and data augmentation. Many algorithms have been proposed for synthetic data generation but reaching the consensus on which method we should use for the specific data sets and use cases remains challenging. Moreover, the majority of existing approaches are ``unsupervised'' in the sense that they do not take into account the downstream task. To address these issues, this work presents a novel synthetic data generation framework. The framework integrates a supervised component tailored to the specific downstream task and employs a meta-learning approach to learn the optimal mixture distribution of existing synthetic distributions.