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We propose an efficient online approximate Bayesian inference algorithm forestimating the parameters of a nonlinear function from a potentiallynon-stationary data stream. The method is based on the extended Kalman filter(EKF), but uses a novel low-rank plus diagonal decomposition of the posteriorprecision matrix, which gives a cost per step which is linear in the number ofmodel parameters. In contrast to methods based on stochastic variationalinference, our method is fully deterministic, and does not require step-sizetuning. We show experimentally that this results in much faster (more sampleefficient) learning, which results in more rapid adaptation to changingdistributions, and faster accumulation of reward when used as part of acontextual bandit algorithm.

Low-rank extended Kalman filtering for online learning of neural networks from streaming data