Reviews: Learning Sample-Specific Models with Low-Rank Personalized Regression

This paper presents a model for performing personalized regression; i.e. allowing for individual prediction models for each sample, rather than estimating a model for a group of samples. This is clearly useful in personalized medicine, but could also be applied in other settings, e.g. The method uses a linear model (although it could be extended to glms). Estimation is made feasible by reducing the p*n space of parameters to a lower-dimensional subspace via factor analysis. Additionally, regularization between parameter vectors for different samples is applied via a learned distance metric consisting of a weighted sum over covariate-specific base distances.