Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with \beta-Divergences
We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with $\beta$-divergences. The resulting inference procedure is doubly robust for both the predictive 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 $\beta \to 0$. Secondly, we give a principled way of choosing the divergence parameter $\beta$ by minimizing expected predictive loss on-line. Reducing False Discovery Rates of \CPs from up to 99\% to 0\% on real world data, this offers the state of the art.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $β$-Divergences
We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with $\beta$-divergences. The resulting inference procedure is doubly robust for both the paramete…
Bayesian InferenceChange Point DetectionDirichlet process mixture models for non-stationary data streams
In recent years, we have seen a handful of work on inference algorithms over non-stationary data streams. Given their flexibility, Bayesian non-parametric models are a good candidate for these scenarios. However, reliabl…
ClusteringDensity EstimationVariational InferenceDoubly Outlier-Robust Online Infinite Hidden Markov Model
We derive a robust update rule for the online infinite hidden Markov model (iHMM) for when the streaming data contains outliers and the model is misspecified. Leveraging recent advances in generalised Bayesian inference,…
Bayesian InferenceContinual Learning with Bayesian Neural Networks for Non-Stationary Data
This work addresses continual learning for non-stationary data, using Bayesian neural networks and memory-based online variational Bayes. We represent the posterior approximation of the network weights by a diagonal Gaus…
Continual LearningStreaming Inference for Infinite Non-Stationary Clustering
Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here, we attack learning under all three cond…
ClusteringVariational Inference