We consider the problem of high-dimensional Bayesian nonparametric variable selection using an aggregation of so-called “weak learners.” The most popular variant of this is the Bayesian additive regression trees (BART) model, which is the natural Bayesian analog to boosting decision trees. In this article, we use Gibbs distributions on random partitions to induce sparsity in ensembles of weak learners. Looking at BART as a special case, we show that the class of Gibbs priors includes two recently proposed models—t…