  Bias in datasets can be very detrimental for appropriate statistical
estimation. In response to this problem, importance weighting methods have been
developed to match any biased distribution to its corresponding target unbiased
distribution. The seminal Kernel Mean Matching (KMM) method is, nowadays, still
considered as state of the art in this research field. However, one of the main
drawbacks of this method is the computational burden for large datasets.
Building on previous works by Huang et al. (2007) and de Mathelin et al.
(2021), we derive a novel importance weighting algorithm which scales to large
datasets by using a neural network to predict the instance weights. We show, on
multiple public datasets, under various sample biases, that our proposed
approach drastically reduces the computational time on large dataset while
maintaining similar sample bias correction performance compared to other
importance weighting methods. The proposed approach appears to be the only one
able to give relevant reweighting in a reasonable time for large dataset with
up to two million data.
