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Synthetic tabular data generation with differential privacy is a crucial problem to enable data sharing with formal privacy.
Approximating discrete probability distributions with dependence trees
C. Chow and C. Liu · 1968
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Differentially private synthesization of multi-dimensional data using copula functions
Haoran Li, Li Xiong, and Xiaoqian Jiang · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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PyTorch: an imperative style, high-performance deep learning library
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Language models are unsupervised multitask learners
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Dp-cgan: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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Modeling tabular data using conditional gan
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2019
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PATE-GAN: Generating synthetic data with differential privacy guarantees
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2019
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Transformers: State-of-the-art natural language processing
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A robust variational autoencoder using beta divergence
Haleh Akrami, Anand A. Joshi, Jian Li, Sergül Aydöre, and Richard M. Leahy · 2021
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Differentially private query release through adaptive projection
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, and Ankit A Siva · 2021
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Copula flows for synthetic data generation
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Iterative methods for private synthetic data: Unifying framework and new methods
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Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2021
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Opacus: User-friendly differential privacy library in PyTorch
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TabMT: Generating tabular data with masked transformers
Manbir S Gulati and Paul F Roysdon · 2023
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Tabllm: Few-shot classification of tabular data with large language models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag · 2023
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Mistral 7b, 2023
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
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Tabddpm: modelling tabular data with diffusion models
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Deep neural networks and tabular data: A survey
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LoRA: Low-rank adaptation of large language models
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Aim: an adaptive and iterative mechanism for differentially private synthetic data
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Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko · 2023
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Harnessing large-language models to generate private synthetic text
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Synthetic data generation with large language models for text classification: Potential and limitations
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Generating private synthetic data with genetic algorithms
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Privately generating tabular data using language models, 2023
Alexandre Sablayrolles, Yue Wang, and Brian Karrer · 2023
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Synthetic text generation with differential privacy: A simple and practical recipe
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Dp-tabicl: In-context learning with differentially private tabular data, 2024
Alycia N. Carey, Karuna Bhaila, Kennedy Edemacu, and Xintao Wu · 2024
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Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
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Uci machine learning repository: Adult data set, 2017
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FFB: A fair fairness benchmark for in-processing group fairness methods
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Harnessing large-language models to generate private synthetic text, 2024
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Controllable tabular data synthesis using diffusion models
Tongyu Liu, Ju Fan, Nan Tang, Guoliang Li, and Xiaoyong Du · 2024
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Controllable data generation by deep learning: A review
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Differentially private synthetic data via foundation model apis 2: Text, 2024
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, and Sergey Yekhanin · 2024
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Quantifying and mitigating privacy risks for tabular generative models, 2024
Chaoyi Zhu, Jiayi Tang, Hans Brouwer, Juan F. Pérez, Marten van Dijk, and Lydia Y. Chen · 2024
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