A Split-and-Conquer Approach for Analysis of Extraordinarily Large Data

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Random Forests for Big Data (2015, methodology): “One variant relies on subsampling while three others are related to parallel implementations of random forests and involve either various adaptations of bootstrap to Big Data or “divide-and-conquer” approaches.”
Communication-efficient sparse regression: a one-shot approach (2015, background): “Chen and Xie (2012) and Wang, Peng and Dunson (2014) also consider sparse linear regression in the distributed setting. They propose an averaging estimator that obtains the same convergence rate as the centralized lasso (lasso on all the data), but relies heavily on the irrepresentable condition and”