  Neural network ensembling is a common and robust way to increase model
efficiency. In this paper, we propose a new neural network ensemble algorithm
based on Audibert's empirical star algorithm. We provide optimal theoretical
minimax bound on the excess squared risk. Additionally, we empirically study
this algorithm on regression and classification tasks and compare it to most
popular ensembling methods.
