用概率电路生成表格数据，成本低且效果优于主流扩散模型。
A Sobering Look at Tabular Data Generation via Probabilistic Circuits
- 采用深度概率电路构建层次混合模型，原生支持异构数据
- 在多个基准上性能媲美甚至超越扩散模型，计算成本仅为几分之一
- 指出现有评估指标误导了进展判断，适合关注生成质量的科研人员
表格数据生成比文本和图像更难，因其特征异质性高且样本量小。当前主流方法为基于扩散的模型，在常用基准上几乎达到完美表现。本文质疑这一进展感知的可靠性：首先指出现有评估协议存在缺陷，倡导新评估方式；其次重新审视一种简单基线——基于深度概率电路的分层混合模型，其在极低计算成本下表现可与当前最优模型媲美甚至更优；概率电路是决策森林的生成对应物，天然支持异构数据，并能实现高效的概率生成与推理。最后，通过严谨实证分析发现，主流模型看似已达性能饱和，实因使用了不当评估指标所致。因此，真实表格式数据生成仍有巨大提升空间。代码见 https://github.com/april-tools/tabpc。
原文摘要 · Abstract (English)
Tabular data is more challenging to generate than text and images, due to its heterogeneous features and much lower sample sizes. On this task, diffusion-based models are the current state-of-the-art (SotA) model class, achieving almost perfect performance on commonly used benchmarks. In this paper, we question the perception of progress for tabular data generation. First, we highlight the limitations of current protocols to evaluate the fidelity of generated data, and advocate for alternative ones. Next, we revisit a simple baseline -- hierarchical mixture models in the form of deep probabilistic circuits (PCs) -- which delivers competitive or superior performance to SotA models for a fraction of the cost. PCs are the generative counterpart of decision forests, and as such can natively handle heterogeneous data as well as deliver tractable probabilistic generation and inference. Finally, in a rigorous empirical analysis we show that the apparent saturation of progress for SotA models is largely due to the use of inadequate metrics. As such, we highlight that there is still much to be done to generate realistic tabular data. Code available at https://github.com/april-tools/tabpc.
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