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Dynamic trees for online analysis of massive data

Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to online real-time information. Learning algorithms deployed in such contexts must rely on single-pass inference, where the data history is never revisited. Although rapidly developing, online nonparametric Bayesian inference remains challenged by massive, transient data, as the complexity of such models will typically increase with the sample size. In this work, we take steps to overcome these challenges by porting data discarding into a fully Bayesian framework via the use of informative priors and active learning heuristics. We showcase our methods by augmenting a modern non-parametric modelling framework, dynamic trees, and illustrate its performance on a number of practical examples. The end product is a powerful fully online regression and classification tool, whose performance compares favorably to the state-of-the-art.