Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
This addresses data scarcity and privacy issues in domains like healthcare and finance, though it is incremental as it builds on existing transfer and meta-learning techniques.
The paper tackles the problem of generating high-quality synthetic tabular data in low-data scenarios by integrating artificial inductive biases into deep generative models, achieving up to 60% gains in Jensen-Shannon divergence with transfer learning methods.
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.