STASY: SCORE-BASED TABULAR DATA SYNTHESIS

Abstract

Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular data. In this paper, we present a new model named Score-based Tabular data Synthesis (STaSy) and its training strategy based on the paradigm of score-based generative modeling. Despite the fact that score-based generative models have resolved many issues in generative models, there still exists room for improvement in tabular data synthesis. Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training. Furthermore, we also conduct rigorous experimental studies in terms of the generative task trilemma: sampling quality, diversity, and time. In our experiments with 15 benchmark tabular datasets and 7 baselines, our method outperforms existing methods in terms of task-dependant evaluations and diversity.



Summary of experimental results. We report the average sampling quality, diversity, and time.OCT-GAN (Kim et al., 2021). In addition, a recent generative model paradigm, called score-based generative modeling (SGMs), successfully resolves the two problems of the generative learning trilemma(Xiao et al., 2021), i.e., score-based generative models provide high sampling quality and diversity, although their training/sampling time is relatively longer than other deep generative models. In this paper, we adopt a score-based generative modeling paradigm and design a Score-based Tabular data Synthesis (STaSy) method.Our model designs significantly outperform all existing baselines in terms of the sampling quality and diversity (cf. Naïve-STaSy and STaSy in Table1) -Naïve-STaSy is a naive conversion

