SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[16] Roderick J. A. Little and Donald B. Rubin. Statistical analysis with missing data, third edition. Wiley Series in Probability and Statistics, 2019.
[17] Yang Liu, Yan Kang, Chaoping Xing, Tianjian Chen, and Qiang Yang. A secure federated transfer learning framework. IEEE Intelligent Systems, 35:70-82, 2020.
[18] Lamin R. Mansaray, Adam Sheka Kanu, Lingbo Yang, Jingfeng Huang, and Fumin Wang. Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery. GIScience & Remote Sensing, 57:785 - 796, 2020.
[19] H. B. McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. Communication-efficient learning of deep networks from decentralized data. In International Conference on Artificial Intelligence and Statistics, 2016. URL https://api.sementicscholar.org/CorpusID:14955348
[20] Board on Physics. A strategy for active remote sensing amid increased demand for radio spectrum. 2015.
[21] Benjamin Recht, Christopher Ré, Stephen J. Wright, and Feng Niu. Hogwild: A lock-free approach to parallelizing stochastic gradient descent. In NIPS, 2011.
[22] Ritvik Sahajpal, Lucas Fontana, Pedro Lafluf, Guillermo Leale, Estefania Puricelli, Dan O'Neill, Mehdi Hosseini, Mauricio Varela, and Inbal Becker-Reshef. Using machine-learning models for field-scale crop yield and condition modeling in argentina. 2020.
[23] Joseph L Schafer. Multiple imputation: a primer. Statistical methods in medical research, 8(1): 3-15, 1999.
[24] Joseph L. Schafer and John W. Graham. Missing data: our view of the state of the art. Psychological methods, 7 2:147-77, 2002.
[25] Joseph L Schafer and Nathaniel Schenker. Inference with imputed conditional means. Journal of the American Statistical Association, 95(449):144-154, 2000.
[26] Ananda Theertha Suresh, Ziteng Sun, Jae Hun Ro, and Felix X. Yu. Correlated quantization for distributed mean estimation and optimization. ArXiv, abs/2203.04925, 2022.
[27] Anastasios A. Tsiatis. Semiparametric theory and missing data. 2006.
[28] Jaideep Vaidya and Chris Clifton. Privacy-preserving k-means clustering over vertically partitioned data. In Knowledge Discovery and Data Mining, 2003.
[29] Naisyin Wang and James M. Robins. Large-sample theory for parametric multiple imputation procedures. Biometrika, 85:935-948, 1998.
[30] Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. Federated machine learning: Concept and applications. arXiv: Artificial Intelligence, 2019.
[31] Hwanjo Yu, Jaideep Vaidya, and Xiaoqian Jiang. Privacy-preserving svm classification on vertically partitioned data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2006.