1. Please find at least 5 research papers for Outlier detection concepts/methods and write a summary using them in 1 page.
2. From the list of papers provided below (related to data imputation): please summarize them in 1 page:
Yoon, J., Jordon, J., & van der Schaar, M. (2018). GAIN: Missing Data Imputation using Generative Adversarial Nets. Retrieved from http://arxiv.org/abs/1806.02920
McCoy, J. T., Kroon, S., & Auret, L. (2018). Variational Autoencoders for Missing Data Imputation with Application to a Simulated Milling Circuit. IFAC-PapersOnLine, 51(21), 141–146. https://doi.org/10.1016/J.IFACOL.2018.09.406
Gondara, L., & Wang, K. (2017). MIDA: Multiple Imputation using Denoising Autoencoders. Retrieved from http://arxiv.org/abs/1705.02737
Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. BIOINFORMATICS ORIGINAL PAPER, 28(1), 112–118. https://doi.org/10.1093/bioinformatics/btr597
Camino, R. D., Hammerschmidt, C. A., & State, R. (2019). Improving Missing Data Imputation with Deep Generative Models. Retrieved from http://arxiv.org/abs/1902.10666
Mayer, I., Josse, J., Tierney, N., & Vialaneix, N. (2019). R-miss-tastic: a unified platform for missing values methods and workflows. Retrieved from https://cran.r-project.org/web/views/MissingData.html
Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (n.d.). Data Cleaning: Overview and Emerging Challenges. https://doi.org/10.1145/2882903.2912574
Nazabal, A., Uk, A. A., Olmos, P. M., Ghahramani, Z., Uk, Z. C. A., & Valera, I. (n.d.). Handling Incomplete Heterogeneous Data using VAEs. Retrieved from https://arxiv.org/pdf/1807.03653.pdf
Mandel J, S. P., M, J, el, & M, G. (2015). A Comparison of Six Methods for Missing Data Imputation. Journal of Biometrics & Biostatistics, 06(01). https://doi.org/10.4172/2155-6180.1000224
García-Laencina, P. J., Sancho-Gómez, J.-L., & Figueiras-Vidal, A. R. (n.d.). Pattern classification with missing data: a review. https://doi.org/10.1007/s00521-009-0295-6
Li, S. C.-X., Jiang, B., & Marlin, B. (2019). MisGAN: Learning from Incomplete Data with Generative Adversarial Networks. Retrieved from http://arxiv.org/abs/1902.09599
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