Can BERT Generate Tables? Exploring MaCoDE’s Approach to Synthetic Data

Table of Links Abstract & Introduction Proposal Classification Target Masked Conditional Density Estimation (MaCoDE) Theoretical Results With Missing Data Experiments Results Related Works Conclusions and Limitations References A1 Proof of Theorem 1 A2 Proof of Proposition 1 A3 Dataset Descriptions A4 Missing Mechanism A5 Experimental Settings for Reproduction A6 Additional Experiments A7 Detailed Experimental Results 3.3 Results Q1. As shown in Table 1, MaCoDE consistently achieves the highest metric scores in both joint distributional similarities and machine learning utility while also achieving competitive performance in marginal distributional similarity. This underscores the effectiveness of the synthetic data generation method based on estimating conditional distribution in preserving the joint statistical fidelity of the original data and enhancing the utility of synthetic data for downstream machine learning tasks. Notably, MaCoDE demonstrates remarkable performance in the feature selection downstream task. Remark 4 (How does MaCoDE achieve the remarkable performance in feature selection?). We attribute the effectiveness of the feature selection downstream task to our emphasis on estimating ‘conditional’ distributions. In Random Forest [4], each node in a decision tree represents a conditional distribution of a variable conditioned on the splits made by the tree up to that node, and the feature importance is determined by the purity of nodes. Therefore, accurately estimating the conditional distribution can lead to higher performance in preserving the feature importance ranking. Q2. The missing data mechanism within the parentheses refers to the mechanism applied to the training dataset on which MaCoDE was trained. Despite encountering missing data scenarios such as MAR, MCAR, MNARL, and MNARQ, Table 2 illustrates MaCoDE’s ability to generate highquality synthetic data regarding machine learning utility while handling incomplete training datasets without significant performance degradation. Even in the presence of missing entries, MaCoDE achieves better metric scores than most baseline models in terms of SMAPE, except for TabDDPM. Additionally, concerning the F1 score, MaCoDE either competes competitively or achieves a higher score than other baseline models. Q3. The missing data mechanism within the parentheses refers to the mechanism applied to the dataset on which MaCoDE was trained. Table 3 indicates that MaCoDE consistently exhibits competitive performance against all baseline models across metrics assessing multiple imputation performances, including bias, coverage, and confidence interval length. This suggests our proposed approach can support multiple imputations for deriving statistically valid inferences from missing data with the MAR mechanism. Sensitivity analysis. We also conducted a sensitivity analysis by varying the missingness rate of kings dataset. Figure 3.(a) illustrates that, in terms of SMAPE, MaCoDE maintains competitive performance even as the missingness rate increases. Concerning the F1 score, MaCoDE outperforms other models at missingness rates of 0.1 and 0.3, but its performance declines beyond a missingness rate of 0.5. Additionally, regarding multiple imputation performance of Figure 3.(b), despite the increase in missingness rate, MaCoDE consistently demonstrates competitive performance compared to other imputation models without significant performance degradation. 4. Related Works Synthetic tabular data generation. Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been employed in synthetic tabular data generation. CTGAN [51], TVAE [51], CTAB-GAN [56], CTAB-GAN+ [57], DistVAE [1], and GOGGLE [31] employ VAE or GAN for their ability to represent complex data distributions. Recently, diffusion-based or score-based generative models, such as TabDDPM [24], STaSy [23], and CoDi [25], have gained popularity due to their noticeable performance in approximating data distributions. However, these models often encounter challenges when dealing with incomplete training datasets as they rely on complete data for training. In contrast, Transformer-based synthesizers like TabPFGen [33], TabMT [13], and REaLTabFormer [45] can handle incomplete datasets. Missing data imputations. Recent methods employ the deep generative model to estimate a joint distribution and generate samples for imputations. GAN-based imputers such as GAIN [52] and MisGAN [30] adopt an adversarial learning approach to generate both missing entries and masking vectors. Other approaches like VAEAC [19], HI-VAE [37], and ReMasker [10] employ strategies that allow them to learn conditional distributions on arbitrary conditioning sets using the uniform masking strategy. MIWAE [34], based on the Importance Weighted Autoencoder [5], demonstrates that under mild conditions and the MAR assumption, the target likelihood can be approximated regardless of the imputation function. Additionally, not-MIWAE [18] extends MIWAE to handle cases where the data is under MNAR assumption by modeling missing entries as a latent variable. In parallel, the Optimal Transport (OT)-based method employs distributional matching, utilizing the 2-Wasserstein distance to compare distributions in both data and latent spaces [36, 54]. To handle mixed-type tabular datasets, [55] introduced EGC (extended Gaussian copula), which relies on a latent Gaussian distribution to support single and multiple imputations. 5. Conclusions and Limitations This paper introduces a novel approach to generating synthetic data for mixed-type tabular datasets. Our proposed method integrates histogram-based non-parametric conditional density estimation and the MLM-based approach. By demonstrating that our conditional density estimators for continuous columns are weakly consistent estimators, we bridge the theoretical gap between distributional learning and the multi-class classification task of MLM. Although our primary goal is to generate synthetic data with high MLu, we empirically demonstrate that we achieve high joint statistical fidelity and MLu simultaneously. Furthermore, empirical experiments validate that our proposed model can generate high-quality synthetic tabular datasets in terms of MLu even when incomplete training datasets are given. However, in our context, ‘arbitrary’ conditional density estimation refers to estimating the conditional density for arbitrary combinations of conditioning sets and target variables rather than accommodating arbitrary types of continuous distributions. For example, if x has lower bounded support and its CDF doesn’t satisfy Assumption 1, then Q(0) = inf{x ∈ R : 0 ≤ F(x)} = −∞, indicating that inverse transform sampling may generate out-of-support samples. Improving our proposed model to accommodate arbitrary types of continuous distributions is our future work. References [1] Seunghwan An and Jong-June Jeon. Distributional learning of variational autoencoder: Application to synthetic data generation. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. 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Frontiers in Big Data, abs/2204.00401, 2023. Authors: (1) Seunghwan An, Department of Statistical Data Science, University of Seoul, S. Korea (dkstmdghks79@uos.ac.kr); (2) Gyeongdong Woo, Department of Statistical Data Science, University of Seoul, S. Korea (dngudxor23@uos.ac.kr); (3) Jaesung Lim, Department of Statistical Data Science, University of Seoul, S. Korea (wotjd1410@uos.ac.kr); (4) ChangHyun Kim, Department of Statistical Data Science, University of Seoul, S. Korea (hahaha503@uos.ac.kr); (5) Sungchul Hong, Department of Statistics, University of Seoul, S. Korea (shong@uos.ac.kr); (6) Jong-June Jeon (corresponding author), Department of Statistics, University of Seoul, S. Korea (jj.jeon@uos.ac.kr). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Table of Links Abstract & Introduction Proposal Classification Target Masked Conditional Density Estimation (MaCoDE) Theoretical Results With Missing Data Experiments Results Related Works Conclusions and Limitations References A1 Proof of Theorem 1 A2 Proof of Proposition 1 A3 Dataset Descriptions A4 Missing Mechanism A5 Experimental Settings for Reproduction A6 Additional Experiments A7 Detailed Experimental Results Abstract & Introduction Abstract & Introduction Abstract & Introduction Proposal Classification Target Masked Conditional Density Estimation (MaCoDE) Proposal Proposal Classification Target Masked Conditional Density Estimation (MaCoDE) Classification Target Masked Conditional Density Estimation (MaCoDE) Theoretical Results With Missing Data Theoretical Results Theoretical Results With Missing Data With Missing Data Experiments Experiments Experiments Results Related Works Conclusions and Limitations References Results Results Related Works Conclusions and Limitations References Related Works Conclusions and Limitations References A1 Proof of Theorem 1 A2 Proof of Proposition 1 A3 Dataset Descriptions A1 Proof of Theorem 1 A1 Proof of Theorem 1 A2 Proof of Proposition 1 A3 Dataset Descriptions A2 Proof of Proposition 1 A3 Dataset Descriptions A4 Missing Mechanism A5 Experimental Settings for Reproduction A4 Missing Mechanism A4 Missing Mechanism A5 Experimental Settings for Reproduction A5 Experimental Settings for Reproduction A6 Additional Experiments A6 Additional Experiments A6 Additional Experiments A7 Detailed Experimental Results A7 Detailed Experimental Results A7 Detailed Experimental Results 3.3 Results Q1. As shown in Table 1, MaCoDE consistently achieves the highest metric scores in both joint distributional similarities and machine learning utility while also achieving competitive performance in marginal distributional similarity. This underscores the effectiveness of the synthetic data generation method based on estimating conditional distribution in preserving the joint statistical fidelity of the original data and enhancing the utility of synthetic data for downstream machine learning tasks. Notably, MaCoDE demonstrates remarkable performance in the feature selection downstream task. Q1. Remark 4 (How does MaCoDE achieve the remarkable performance in feature selection?). We attribute the effectiveness of the feature selection downstream task to our emphasis on estimating ‘conditional’ distributions. In Random Forest [4], each node in a decision tree represents a conditional distribution of a variable conditioned on the splits made by the tree up to that node, and the feature importance is determined by the purity of nodes. Therefore, accurately estimating the conditional distribution can lead to higher performance in preserving the feature importance ranking. Remark 4 (How does MaCoDE achieve the remarkable performance in feature selection?). We attribute the effectiveness of the feature selection downstream task to our emphasis on estimating ‘conditional’ distributions. In Random Forest [4], each node in a decision tree represents a conditional distribution of a variable conditioned on the splits made by the tree up to that node, and the feature importance is determined by the purity of nodes. Therefore, accurately estimating the conditional distribution can lead to higher performance in preserving the feature importance ranking. Q2. The missing data mechanism within the parentheses refers to the mechanism applied to the training dataset on which MaCoDE was trained. Despite encountering missing data scenarios such as MAR, MCAR, MNARL, and MNARQ, Table 2 illustrates MaCoDE’s ability to generate highquality synthetic data regarding machine learning utility while handling incomplete training datasets without significant performance degradation. Even in the presence of missing entries, MaCoDE achieves better metric scores than most baseline models in terms of SMAPE, except for TabDDPM. Additionally, concerning the F1 score, MaCoDE either competes competitively or achieves a higher score than other baseline models. Q2. Q3. The missing data mechanism within the parentheses refers to the mechanism applied to the dataset on which MaCoDE was trained. Table 3 indicates that MaCoDE consistently exhibits competitive performance against all baseline models across metrics assessing multiple imputation performances, including bias, coverage, and confidence interval length. This suggests our proposed approach can support multiple imputations for deriving statistically valid inferences from missing data with the MAR mechanism. Q3. Sensitivity analysis. We also conducted a sensitivity analysis by varying the missingness rate of kings dataset. Figure 3.(a) illustrates that, in terms of SMAPE, MaCoDE maintains competitive performance even as the missingness rate increases. Concerning the F1 score, MaCoDE outperforms other models at missingness rates of 0.1 and 0.3, but its performance declines beyond a missingness rate of 0.5. Additionally, regarding multiple imputation performance of Figure 3.(b), despite the increase in missingness rate, MaCoDE consistently demonstrates competitive performance compared to other imputation models without significant performance degradation. Sensitivity analysis. 4. Related Works Synthetic tabular data generation. Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been employed in synthetic tabular data generation. CTGAN [51], TVAE [51], CTAB-GAN [56], CTAB-GAN+ [57], DistVAE [1], and GOGGLE [31] employ VAE or GAN for their ability to represent complex data distributions. Recently, diffusion-based or score-based generative models, such as TabDDPM [24], STaSy [23], and CoDi [25], have gained popularity due to their noticeable performance in approximating data distributions. However, these models often encounter challenges when dealing with incomplete training datasets as they rely on complete data for training. In contrast, Transformer-based synthesizers like TabPFGen [33], TabMT [13], and REaLTabFormer [45] can handle incomplete datasets. Synthetic tabular data generation. Missing data imputations. Recent methods employ the deep generative model to estimate a joint distribution and generate samples for imputations. GAN-based imputers such as GAIN [52] and MisGAN [30] adopt an adversarial learning approach to generate both missing entries and masking vectors. Other approaches like VAEAC [19], HI-VAE [37], and ReMasker [10] employ strategies that allow them to learn conditional distributions on arbitrary conditioning sets using the uniform masking strategy. MIWAE [34], based on the Importance Weighted Autoencoder [5], demonstrates that under mild conditions and the MAR assumption, the target likelihood can be approximated regardless of the imputation function. Additionally, not-MIWAE [18] extends MIWAE to handle cases where the data is under MNAR assumption by modeling missing entries as a latent variable. In parallel, the Optimal Transport (OT)-based method employs distributional matching, utilizing the 2-Wasserstein distance to compare distributions in both data and latent spaces [36, 54]. To handle mixed-type tabular datasets, [55] introduced EGC (extended Gaussian copula), which relies on a latent Gaussian distribution to support single and multiple imputations. Missing data imputations. 5. Conclusions and Limitations This paper introduces a novel approach to generating synthetic data for mixed-type tabular datasets. Our proposed method integrates histogram-based non-parametric conditional density estimation and the MLM-based approach. By demonstrating that our conditional density estimators for continuous columns are weakly consistent estimators, we bridge the theoretical gap between distributional learning and the multi-class classification task of MLM. Although our primary goal is to generate synthetic data with high MLu, we empirically demonstrate that we achieve high joint statistical fidelity and MLu simultaneously. Furthermore, empirical experiments validate that our proposed model can generate high-quality synthetic tabular datasets in terms of MLu even when incomplete training datasets are given. However, in our context, ‘arbitrary’ conditional density estimation refers to estimating the conditional density for arbitrary combinations of conditioning sets and target variables rather than accommodating arbitrary types of continuous distributions. For example, if x has lower bounded support and its CDF doesn’t satisfy Assumption 1, then Q(0) = inf{x ∈ R : 0 ≤ F(x)} = −∞, indicating that inverse transform sampling may generate out-of-support samples. Improving our proposed model to accommodate arbitrary types of continuous distributions is our future work. References [1] Seunghwan An and Jong-June Jeon. Distributional learning of variational autoencoder: Application to synthetic data generation. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. [2] Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, and Alexandre Drouin. TACTis-2: Better, faster, simpler attentional copulas for multivariate time series. In The Twelfth International Conference on Learning Representations, 2024. [3] Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci. Deep neural networks and tabular data: A survey. 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Korea (jj.jeon@uos.ac.kr). Authors : Authors (1) Seunghwan An, Department of Statistical Data Science, University of Seoul, S. Korea (dkstmdghks79@uos.ac.kr); (2) Gyeongdong Woo, Department of Statistical Data Science, University of Seoul, S. Korea (dngudxor23@uos.ac.kr); (3) Jaesung Lim, Department of Statistical Data Science, University of Seoul, S. Korea (wotjd1410@uos.ac.kr); (4) ChangHyun Kim, Department of Statistical Data Science, University of Seoul, S. Korea (hahaha503@uos.ac.kr); (5) Sungchul Hong, Department of Statistics, University of Seoul, S. Korea (shong@uos.ac.kr); (6) Jong-June Jeon (corresponding author), Department of Statistics, University of Seoul, S. Korea (jj.jeon@uos.ac.kr). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv available on arxiv