Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This enables scalable, real-time Bayesian nonparametric inference for applications requiring distributed data processing, though it is incremental relative to existing variational methods.
The paper tackles the challenge of developing streaming, distributed inference for Bayesian nonparametric models by proposing a framework that processes data minibatches asynchronously without learning rates or truncation. It demonstrates practical scalability and performance in experiments with a DP mixture model.
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes receive a sequence of data minibatches, compute a variational posterior for each, and make asynchronous streaming updates to a central model. In contrast to previous algorithms, the proposed framework is truly streaming, distributed, asynchronous, learning-rate-free, and truncation-free. The key challenge in developing the framework, arising from the fact that BNP models do not impose an inherent ordering on their components, is finding the correspondence between minibatch and central BNP posterior components before performing each update. To address this, the paper develops a combinatorial optimization problem over component correspondences, and provides an efficient solution technique. The paper concludes with an application of the methodology to the DP mixture model, with experimental results demonstrating its practical scalability and performance.