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Data imputation and data generation have important applications for many domains, like healthcare and finance, where incomplete or missing data can hinder accurate analysis and decision-making.
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DataWig: Missing Value Imputation for Tables
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CTAB-GAN: Effective Table Data Synthesizing. In Proceedings of The 13th Asian Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 157) , Vineeth N. Balasubramanian and Ivor Tsang (Eds.). PMLR, 97–112
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Deep Neural Networks and Tabular Data: A Survey
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How to deal with missing data in supervised deep learning?. In ICLR 2022-10th International Conference on Learning Representations
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HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 162) , Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.). PMLR, 9916–9937
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Diffusion models for missing value imputation in tabular data. In NeurIPS 2022 First Table Representation Workshop
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STaSy: Score-based Tabular data Synthesis. In The Eleventh International Conference on Learning Representations
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Why do tree-based models still outperform deep learning on typical tabular data?. In Proceedings of the 36th International Conference on Neural Information Processing Systems (New Orleans, LA, USA) (NIPS ’22) . Curran Associates Inc., Red Hook, NY, USA, Article 37, 14 pages
Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux. 2024 · 2024
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Evaluation metrics and statistical tests for machine learning
Oona Rainio, Jarmo Teuho, and Riku Klén. 2024 · 2024
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