让生成表格数据自动满足复杂约束，解决模型常违规问题。
Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
- 引入析取精炼层，自动使生成模型满足非凸、不连通的线性约束
- 在违反约束的模型上消除全部违规，F1提升最高21.4%，AUC提升20.9%
- 适合需要严格遵守业务规则的数据生成场景
合成表格数据生成因底层分布高度复杂而长期面临挑战。尽管深度生成模型（DGMs）取得进展，现有方法仍难以生成与背景知识一致的合理数据点。本文提出析取精炼层（DRL），首次实现深度学习模型对量词消去线性公式（可定义非凸甚至不连通空间）的自动合规。实验表明，DRL不仅保证约束满足，还显著提升下游任务性能：在频繁违反约束的模型上完全消除违规，且在F1-score上提升最高达21.4%，在ROC曲线下面积（AUC）上提升最高达20.9%，展现出实际应用价值。
原文摘要 · Abstract (English)
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.
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