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Tabular data is a common form of organizing data.
Markov source modeling of text generation
Jelinek, F · 1985
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A neural probabilistic language model
Bengio, Y., Ducharme, R., and Vincent, P · 2000
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Differential privacy and machine learning: a survey and review
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The Synthetic Data Vault
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Privacy-preserving tabular data publishing: a comprehensive evaluation from web to cloud
Abdelhameed, S. A., Moussa, S. M., and Khalifa, M. E · 2018
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Random forest versus logistic regression: a large-scale benchmark experiment
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Data synthesis based on generative adversarial networks
Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., and Kim, Y · 2018
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Catboost: unbiased boosting with categorical features
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A · 2018
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Validation of agent-based models in economics and finance
Fagiolo, G., Guerini, M., Lamperti, F., Moneta, A., and Roventini, A · 2019
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Machine learning for comprehensive forecasting of alzheimer’s disease progression
Fisher, C. K., Smith, A. M., and Walsh, J. R · 2019
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Evaluating variational autoencoder as a private data release mechanism for tabular data
Li, S.-C., Tai, B.-C., and Huang, Y · 2019
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Opitz, J. and Burst, S · 2019
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Language Models are Unsupervised Multitask Learners
Synthesising multi-modal minority samples for tabular data
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Revisiting deep learning models for tabular data
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A · 2021
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Privacy-preserving household characteristic identification with federated learning method
Lin, J., Ma, J., and Zhu, J · 2021
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Tabular Transformers for Modeling Multivariate Time Series
Padhi, I., Schiff, Y., Melnyk, I., Rigotti, M., Mroueh, Y., Dognin, P., Ross, J., Nair, R., and Altman, E · 2021
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Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I · 2021
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Ctab-gan: Effective table data synthesizing
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Modeling tabular data using conditional GAN
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K · 2019
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Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
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Are transformers universal approximators of sequence-to-sequence functions?
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Ctab-gan+: Enhancing tabular data synthesis
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Generation and evaluation of synthetic patient data
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A non-parametric test to detect data-copying in generative models
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Zhao, Z., Kunar, A., Birke, R., and Chen, L. Y · 2021
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Privacy and utility of private synthetic data for medical data analyses
Appenzeller, A., Leitner, M., Philipp, P., Krempel, E., and Beyerer, J · 2022
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Language Models are Realistic Tabular Data Generators, October 2022
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Survey on synthetic data generation, evaluation methods and gans
Figueira, A. and Vaz, B · 2022
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Row conditional-tgan for generating synthetic relational databases
Gueye, M., Attabi, Y., and Dumas, M · 2022
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Synthetic data generation for tabular health records: A systematic review
Hernandez, M., Epelde, G., Alberdi, A., Cilla, R., and Rankin, D · 2022
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Tabddpm: Modelling tabular data with diffusion models
Kotelnikov, A., Baranchuk, D., Rubachev, I., and Babenko, A · 2022
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Tabular data: Deep learning is not all you need
Shwartz-Ziv, R. and Armon, A · 2022
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Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramèr, F., Balle, B., Ippolito, D., and Wallace, E · 2023
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