Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios

TL;DR

This paper introduces a novel approach that integrates artificial inductive biases into deep generative models to enhance synthetic tabular data quality in low-data scenarios, leveraging transfer and meta-learning techniques.

Contribution

The paper proposes a new methodology that explicitly incorporates artificial inductive biases into DGMs, improving synthetic data generation in data-scarce environments.

Findings

Transfer learning methods outperform meta-learning in data quality.

Incorporating inductive bias improves performance by up to 60% in Jensen-Shannon divergence.

The approach is model-agnostic and applicable in healthcare and finance domains.

Abstract

While synthetic tabular data generation using Deep Generative Models (DGMs) offers a compelling solution to data scarcity and privacy concerns, their effectiveness relies on the availability of substantial training data, often lacking in real-world scenarios. To overcome this limitation, we propose a novel methodology that explicitly integrates artificial inductive biases into the generative process to improve data quality in low-data regimes. Our framework leverages transfer learning and meta-learning techniques to construct and inject informative inductive biases into DGMs. We evaluate four approaches (pre-training, model averaging, Model-Agnostic Meta-Learning (MAML), and Domain Randomized Search (DRS)) and analyze their impact on the quality of the generated text. Experimental results show that incorporating inductive bias substantially improves performance, with transfer learning methods outperforming meta-learning, achieving up to 60\% gains in Jensen-Shannon divergence. The methodology is model-agnostic and especially relevant in domains such as healthcare and finance, where high-quality synthetic data are essential, and data availability is often limited.