[17] Joshua Snoke, Gillian M Raab, Beata Nowok, Chris Dibben, and Aleksandra Slavkovic. General and specific utility measures for synthetic data. Journal of the Royal Statistical Society. Series A (Statistics in Society), 181(3):663-688, 2018.
[18] Nabeel Seedat, Jonathan Crabbé, Ioana Bica, and Mihaela van der Schaar. Data-iq: Characterizing subgroups with heterogeneous outcomes in tabular data. In Advances in Neural Information Processing Systems, 2022.
[19] Chirag Agarwal, Daniel D'souza, and Sara Hooker. Estimating example difficulty using variance of gradients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10368-10378, 2022.
[20] Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci. Deep neural networks and tabular data: A survey. arXiv preprint arXiv:2110.01889, 2021.
[21] Ravid Shwartz-Ziv and Amitai Armon. Tabular data: Deep learning is not all you need. Information Fusion, 81:84-90, 2022.
[22] Kaggle. Kaggle machine learning and data science survey, 2017.
[23] Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G Willcocks. Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE transactions on pattern analysis and machine intelligence, 2021.
[24] Jessamyn Dahmen and Diane Cook. Synsys: A synthetic data generation system for healthcare applications. Sensors, 19(5):1181, 2019.
[25] Jim Young, Patrick Graham, and Richard Penny. Using bayesian networks to create synthetic data. Journal of Official Statistics, 25(4):549, 2009.
[26] Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. Modeling tabular data using conditional gan. Advances in Neural Information Processing Systems, 32, 2019.
[27] Jaewoo Lee, Minjung Kim, Yonghyun Jeong, and Youngmin Ro. Differentially private normalizing flows for synthetic tabular data generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 7345-7353, 2022.
[28] Danilo Rezende and Shakir Mohamed. Variational inference with normalizing flows. In International conference on machine learning, pages 1530-1538. PMLR, 2015.
[29] Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko. Tabddpm: Modelling tabular data with diffusion models. arXiv preprint arXiv:2209.15421, 2022.
[30] Ahmed Alaa, Boris Van Breugel, Evgeny S Saveliev, and Mihaela van der Schaar. How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models. In International Conference on Machine Learning, pages 290-306. PMLR, 2022.
[31] Tariq Alkhalifah, Hanchen Wang, and Oleg Ovcharenko. Mlreal: Bridging the gap between training on synthetic data and real data applications in machine learning. Artificial Intelligence in Geosciences, 3:101-114, 2022.
[32] Tingwei Shen, Ganning Zhao, and Suya You. A study on improving realism of synthetic data for machine learning. arXiv preprint arXiv:2304.12463, 2023.
[33] Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi. Dataset cartography: Mapping and diagnosing datasets with training dynamics. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9275-9293, 2020.
[34] Curtis Northcutt, Lu Jiang, and Isaac Chuang. Confident learning: Estimating uncertainty in dataset labels. Journal of Artificial Intelligence Research, 70:1373-1411, 2021.