We propose a unifying framework for methods that perform Bayesian online learning in non-stationary environments. We call the framework BONE, which stands for (B)ayesian (O)nline learning in (N)on-stationary (E)nvironments. BONE provides a common structure to tackle a variety of problems, including online continual learning, prequential forecasting, and contextual bandits. The framework requires specifying three modelling choices: (i) a model for measurements (e.g., a neural network), (ii) an auxiliary process to model non-stationarity (e.g., the time since the last changepoint), and (iii) a conditional prior over model parameters (e.g., a multivariate Gaussian). The framework also requires two algorithmic choices, which we use to carry out approximate inference under this framework: (i) an algorithm to estimate beliefs (posterior distribution) about the model parameters given the auxiliary variable, and (ii) an algorithm to estimate beliefs about the auxiliary variable. We show how this modularity allows us to write many different existing methods as instances of BONE; we also use this framework to propose a new method. We then experimentally compare existing methods with our proposed new method on several datasets; we provide insights into the situations that make one method more suitable than another for a given task.

翻译：我们提出了一个用于在非平稳环境中执行贝叶斯在线学习的方法的统一框架。我们称该框架为BONE，代表（B）ayesian（O）nline learning in（N）on-stationary（E）nvironments（非平稳环境下的贝叶斯在线学习）。BONE为处理各种问题提供了一个通用结构，包括在线持续学习、序贯预测和上下文赌博机。该框架需要指定三个建模选择：（i）测量模型（例如神经网络），（ii）用于建模非平稳性的辅助过程（例如自上次变点以来的时间），以及（iii）模型参数的条件先验（例如多元高斯分布）。该框架还需要两个算法选择，我们利用它们在此框架下执行近似推断：（i）用于估计给定辅助变量时模型参数信念（后验分布）的算法，以及（ii）用于估计辅助变量信念的算法。我们展示了这种模块化如何使我们能够将许多不同的现有方法表述为BONE的实例；我们也利用此框架提出了一种新方法。随后，我们在多个数据集上对现有方法与我们所提新方法进行了实验比较；我们深入分析了在特定任务中，何种情境使得一种方法比另一种更为适用。