We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream. The method is based on the extended Kalman filter (EKF), but uses a novel low-rank plus diagonal decomposition of the posterior precision matrix, which gives a cost per step which is linear in the number of model parameters. In contrast to methods based on stochastic variational inference, our method is fully deterministic, and does not require step-size tuning. We show experimentally that this results in much faster (more sample efficient) learning, which results in more rapid adaptation to changing distributions, and faster accumulation of reward when used as part of a contextual bandit algorithm.

翻译：我们提出了一种高效的在线近似贝叶斯推断算法，用于从可能非平稳的数据流中估计非线性函数的参数。该方法基于扩展卡尔曼滤波（EKF），但采用了一种新颖的低秩加对角分解的后验精度矩阵，使得每一步的计算成本与模型参数数量呈线性关系。与基于随机变分推断的方法不同，我们的方法是完全确定性的，并且无需调整步长。实验结果表明，该方法实现了更快（样本效率更高）的学习，从而能够更快速地适应分布变化，并且在用于情境赌博机算法时，能够更快地累积奖励。