Distributed Structured Prediction for Big Data

The biggest limitations of learning structured predictors from big data are the computation time and the memory demands. In this paper, we propose to handle those big data problems efficiently by distributing and parallelizing the resource requirements. We present a distributed structured prediction learning algorithm for large scale models that cannot be effectively handled by a single cluster node. Importantly, convergence and optimality guarantees of recently developed algorithms are preserved while keeping between node communication low.