Affiliation:
1. KLATASDS – MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Basic Research Project of Shanghai Science and Technology Commission
Subject
Applied Mathematics,Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Analysis
Reference36 articles.
1. Imputation of missing data with class imbalance using conditional generative adversarial networks
2. Camino R. D. Hammerschmidt C. A. & State R. (2019). Improving missing data imputation with deep generative models. arXiv preprint arXiv:1902.10666v1.
3. Dalca A. V. Guttag J. & Sabuncu M. R. (2019). Unsupervised data imputation via variational inference of deep subspaces. arXiv preprint arXiv:1903.03503v1.
4. Deng G. Han C. & Matteson D. S. (2020). Learning to rank with missing data via generative adversarial networks. arXiv preprint arXiv:2011.02089v2.
5. Sure independence screening for ultrahigh dimensional feature space