Distributed Bayesian piecewise sparse linear models

This paper proposes a distributed factorized asymptotic Bayesian (FAB) inference of learning piece-wise sparse linear models on distributed memory architectures. The distributed FAB inference solves the simultaneous model selection issue without communicating O(N) data where N is the number of training samples and achieves linear scale-out against the number of CPU cores. It unlocks the limitation of their applicability to middle-scale data sets due to high computational cost for simultaneous determinations of the number of “pieces” and cardinality of each linear predictor. Experimental results demonstrate that the distributed FAB inference achieves high prediction accuracy and performance scalability with benchmark data.