Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
<|reference_start|>Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios: While synthetic tabular data generation using Deep Generative Models (DGMs) offers a compelling solution to data scarcity and privacy concerns, their effectiveness relies on substantial training data, often unavailable in real-world applications. This paper addresses this challenge by proposing a novel methodology for generating realistic and reliable synthetic tabular data with DGMs in limited real-data environments. Our approach proposes several ways to generate an artificial inductive bias in a DGM through transfer learning and meta-learning techniques. We explore and compare four different methods within this framework, demonstrating that transfer learning strategies like pre-training and model averaging outperform meta-learning approaches, like Model-Agnostic Meta-Learning, and Domain Randomized Search. We validate our approach using two state-of-the-art DGMs, namely, a Variational Autoencoder and a Generative Adversarial Network, to show that our artificial inductive bias fuels superior synthetic data quality, as measured by Jensen-Shannon divergence, achieving relative gains of up to 50\% when using our proposed approach. This methodology has broad applicability in various DGMs and machine learning tasks, particularly in areas like healthcare and finance, where data scarcity is often a critical issue.<|reference_end|>
@article{apellániz2024artificial,
    title={Artificial Inductive Bias for Synthetic Tabular Data Generation in
  Data-Scarce Scenarios},
    author={Patricia A. Apell'aniz and Ana Jim'enez and Borja Arroyo Galende and
  Juan Parras and Santiago Zazo},
    journal={arXiv preprint arXiv:2407.03080},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03080},
    primaryClass={cs.LG cs.AI}
}