Missing Data Imputation using Optimal Transport

10 Feb 2020Boris MuzellecJulie JosseClaire BoyerMarco Cuturi

Missing data is a crucial issue when applying machine learning algorithms to real-world datasets. Starting from the simple assumption that two batches extracted randomly from the same dataset should share the same distribution, we leverage optimal transport distances to quantify that criterion and turn it into a loss function to impute missing data values... (read more)

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