Missing value imputation: a review and analysis of the literature (2006–2017)

By Wei‐Chao Lin, Chih-Fong Tsai. Artificial Intelligence Review, 2019

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Language Models are Realistic Tabular Data Generators (2022, background): “…(ii) contain critical person-related information and cannot be shared due to privacy protection or socio-ethical principles (Gasc´on et al., 2017), and (iii) often come with impurity issues such as noisy or missing values which impede the application of modern ML algorithms (Lin & Tsai, 2020).”
A survey on missing data in machine learning (2021, methodology): “Expectation estimates missing values given observed data, whereas in maximisation, the present estimated values are used to maximize the probability of all the data [51].”
Text embedding models can be great data engineers (2025, unknown): “Feature engineering and extraction steps deal with identifying features of the input timeseries encoding the information most relevant to the predictive analytics task at hand [27].”