Beyond the convexity assumption: realistic tabular data generation under quantifier-free real linear constraints
File(s)
Published version
Author(s)
Stoian, Mihaela Cătălina
Giunchiglia, Eleonora
Type
Conference Paper
Abstract
Synthetic tabular data generation has traditionally been a challenging problem due to the high complexity of the underlying distributions that characterise this type of data. Despite recent advances in deep generative models (DGMs), existing methods often fail to produce realistic datapoints that are well-aligned with available background knowledge. In this paper, we address this limitation by introducing Disjunctive Refinement Layer (DRL), a novel layer designed to enforce the alignment of generated data with the background knowledge specified in user-defined constraints. DRL is the first method able to automatically make deep learning models inherently compliant with constraints as expressive as quantifier-free linear formulas, which can define non-convex and even disconnected spaces. Our experimental analysis shows that DRL not only guarantees constraint satisfaction but also improves efficacy in downstream tasks. Notably, when applied to DGMs that frequently violate constraints, DRL eliminates violations entirely. Further, it improves performance metrics by up to 21.4% in F1-score and 20.9% in Area Under the ROC Curve, thus demonstrating its practical impact on data generation.
Date Issued
2025-02-25
Date Acceptance
2025-01-22
Citation
The Thirteenth International Conference on Learning Representations (ICLR), 2025
Publisher
ICLR
Journal / Book Title
The Thirteenth International Conference on Learning Representations (ICLR)
Copyright Statement
© 2025 The Author(s). Available open access under a CC-BY licence (https://creativecommons.org/licenses/by/4.0/)
License URL
Source
International Conference on Learning Representations (ICLR)
Publication Status
Published
Start Date
2025-04-24
Finish Date
2025-04-28
Coverage Spatial
Singapore