Reviews: Group Additive Structure Identification for Kernel Nonparametric Regression

The paper considers kernel regression in the high dimensional setting. The starting point of the paper is the "additive model", which consider regressors on the form f sum_p f_p(X_p) i.e. correlations between different dimensions are ignored. Obviously it is problematic to ignore such correlations so the authors instead consider the "group additive model", where different dimensions are grouped, such that correlations between some dimensions can be modeled. The authors provide a formal treatment of this setting, and provide some (somewhat) ad hoc algorithms for finding optimal groupings. Results are presented for small synthetic and real-world cases.