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Bao Ngoc Vi, Cao Truong Tran, Chi Cong Nguyen                                    21


                      2.  M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial net-works. In
                         International conference on machine learning, pages 214 223. PMLR,2017
                      3.  S. Arora, R. Ge, Y. Liang, T. Ma, and Y. Zhang. Generalization and equilibriumin generative
                         adversarial nets (gans). In International Conference on MachineLearning, pages 224 232.
                         PMLR, 2017.
                      4.  A. Asuncion and D. Newman. UCI machine learning repository, 2007.
                      5.  X. Y. Chaoyue Wang, Chang Xu and D. Tao. Evolutionary generative adversarialnetworks.
                         arXiv preprint arXiv:1803.00657, 2018.
                      6.  P. J. Garc -Laencina, J.-L. Sancho-G omez, and A. R. Figueiras-Vidal. Patternclassifica-
                         tion with missing data: a review. Neural Computing and Applications,19:263 282, 2010.
                      7.  I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair,A. Courville,
                         and Y. Bengio. Generative adversarial nets. In Z. Ghahramani,M. Welling, C. Cortes, N.
                         Lawrence, and K. Q. Weinberger, editors, Advances inNeural Information Processing Sys-
                         tems, volume 27. Curran Associates, Inc., 2014.
                      8.  J. M. Jerez, I. Molina, P. J. Garc -Laencina, E. Alba, N. Ribelles, M. Mart
                         Missing data imputation using statistical and machine learning methodsin a real breast can-
                         cer problem. Artificial intelligence in medicine, 50:105 115,2010.
                      9.  X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. Paul Smolley. Least squares gen-erative
                         adversarial networks. In Proceedings of the IEEE international conferenceon computer vi-
                         sion, pages 2794 2802, 2017.
                      10. V. Nagarajan and J. Z. Kolter. Gradient descent gan optimization is locally stable.arXiv pre-
                         print arXiv:1706.04156, 2017.
                      11. A. Nguyen, J. Clune, Y. Bengio, A. Dosovitskiy, and J. Yosinski. Plug & playgenerative
                         networks: Conditional iterative generation of images in latent space. InProceedings of the
                         IEEE Conference on Computer Vision and Pattern Recognition,pages 4467 4477, 2017.
                      12. T. D. Nguyen, T. Le, H. Vu, and D. Phung. Dual discriminator generative adver-sarial nets.
                         arXiv preprint arXiv:1709.03831, 2017.
                      13. J. R. Quinlan. C4. 5: programs for machine learning. Elsevier, 2014.
                      14. J. A. C. Sterne, I. R. White, J. B. Carlin, M. Spratt, P. Royston, M. G. Ken-ward, A. M.
                         Wood, and J. R. Carpenter. Multiple imputation for missing datain epidemiological and clin-
                         ical research: potential and pitfalls. Bmj, 338(jun29 1),2009.
                      15. B. M. M. Steven Cheng-Xian Li, Bo Jiang. Misgan: Learning from incomplete datawith
                         generative adversarial networks. arXiv preprint arXiv:1902.09599, 2019.
                      16. D. Talwar, A. Mongia, D. Sengupta, and A. Majumdar. Autoimpute: Autoencoderbased im-
                         putation of single-cell rna-seq data. Scientific Reports, 8(1), 2018
                      17. C. T. Tran, M. Zhang, and P. Andreae. Multiple imputation for missing data usinggenetic
                         programming. In Proceedings of the 2015 Annual Conference on Geneticand Evolutionary
                                                           590,  New  York,  NY,  USA,2015.  Association  for
                         Computing Machinery.
                      18. C. T. Tran, M. Zhang, P. Andreae, and B. Xue. Multiple imputation and geneticprogram-
                         ming for classification with incomplete data. In Proceedings of the Geneticand Evolutionary
                         Computation Conference, pages 521 528, 2017.
                      19. N. Tsikriktsis. A review of techniques for treating missing data in om surveyresearch. Jour-
                         nal of Operations Management, 24:53 62, 2005.
                      20. I. Yeh. Uci machine learning repository: Data set. Irvine: University of California,2007.
                      21. J. Yoon, J. Jordon, and M. van der Schaar. GAIN: missing data imputation usinggenerative
                         adversarial nets. CoRR, abs/1806.02920, 2018.








                     ISBN: 978-604-80-8083-9                                                  CITA 2023
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