Reviews: Diverse Ensemble Evolution: Curriculum Data-Model Marriage

This paper proposes a new technique for training ensembles of predictors for supervised-learning tasks. Their main insight is to train individual members of the ensemble in a manner such that they specialize on different parts of the dataset reducing redundancy amongst members and better utilizing the capacity of the individual members. The hope is that ensembles formed out of such predictors will perform better than traditional ensembling techniques. The proposed technique explicitly enforces diversity in two ways: 1. inter-model diversity which makes individual models (predictors) different from each other and 2. intra-model diversity which makes predictors choose data points which are not all similar to each other so that they don't specialize in a very narrow region of the data distribution. This is posed as a bipartite graph matching problem which aims to find a matching between samples and models by selecting edges such that the smallest sum of edge costs is chosen (this is inverted to a maximization problem by subtracting from the highest constant cost one can have on the edges.) To avoid degenerate assignments another matching constraint is introduced which restricts the size of samples selected by each model as well.