2018/06/30 by Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas · 1 citation
Mathematics · Computer Science · #stat.ML #cs.LG
published as Neural Information Processing Systems (NeurIPS) 2018 · 39 pages, 11 figures, published at Neural Information Processing Systems (NeurIPS) 2018
arxiv created 2018/11/27 · arxiv updated 2018/11/28
We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with β-divergences. The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a construction for exponential models and demonstrate it on the Bayesian Linear Regression model. In so doing, we make two additional contributions: Firstly, we make GBI scalable using Structural Variational approximations that are exact as β→ 0. Secondly, we give a principled way of choosing the divergence parameter β by minimizing expected predictive loss on-line. Reducing False Discovery Rates of CPs from more than 90% to 0% on real world data, this offers the state of the art.