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Recent advances in tabular data generation have greatly enhanced synthetic data quality.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Earlier work this paper cites.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aäron van den Oord, and Oriol Vinyals · 2019
Earlier work this paper cites.
Modeling tabular data using conditional gan
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Generating synthetic data in finance: Opportunities, challenges and pitfalls
Samuel A. Assefa, Danial Dervovic, Mahmoud Mahfouz, Robert E. Tillman, Prashant Reddy, and Manuela Veloso · 2021
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
Cited alongside, same era.
Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
Cited alongside, same era.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Cited alongside, same era.
Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Cited alongside, same era.
How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Diffusion models for missing value imputation in tabular data
Shuhan Zheng and Nontawat Charoenphakdee · 2022
Later among the works it cites.
Align your latents: High-resolution video synthesis with latent diffusion models
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis · 2023
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Language models are realistic tabular data generators
Vadim Borisov, Kathrin Sessler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci · 2023
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Tabular and latent space synthetic data generation: a literature review
Joao Fonseca and Fernando Bacao · 2023
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Ahmed Alaa, Boris Van Breugel, Evgeny S Saveliev, and Mihaela van der Schaar · 2022
Cited alongside, same era.
Synthetic data generation for tabular health records: A systematic review
Mikel Hernandez, Gorka Epelde, Ane Alberdi, Rodrigo Cilla, and Debbie Rankin · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
Sos: Score-based oversampling for tabular data
Jayoung Kim, Chaejeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, and Jihoon Cho · 2022
Cited alongside, same era.
Improving generative adversarial networks via adversarial learning in latent space
Yang Li, Yichuan Mo, Liangliang Shi, and Junchi Yan · 2022
Cited alongside, same era.
Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
Cited alongside, same era.
Audioldm: Text-to-audio generation with latent diffusion models
Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley
Cited in the paper.
Jayoung Kim, Chaejeong Lee, and Noseong Park · 2023
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Tabddpm: Modelling tabular data with diffusion models
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko · 2023
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Codi: Co-evolving contrastive diffusion models for mixed-type tabular synthesis
Chaejeong Lee, Jayoung Kim, and Noseong Park · 2023
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Difformer: Scalable (graph) transformers induced by energy constrained diffusion
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan · 2023
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Towards principled assessment of tabular data synthesis algorithms
Yuntao Du and Ninghui Li · 2024
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