We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity. We derive a new Bayesian online inference approach to simultaneously infer these distribution shifts and adapt the model to the detected changes by integrating ideas from change point detection, switching dynamical systems, and Bayesian online learning. Using a binary 'change variable,' we construct an informative prior such that--if a change is detected--the model partially erases the information of past model updates by tempering to facilitate adaptation to the new data distribution. Furthermore, the approach uses beam search to track multiple change-point hypotheses and selects the most probable one in hindsight. Our proposed method is model-agnostic, applicable in both supervised and unsupervised learning settings, suitable for an environment of concept drifts or covariate drifts, and yields improvements over state-of-the-art Bayesian online learning approaches.

翻译：我们考虑到在分配变化发生速度不明和强度不明的情况下在线学习的问题。 我们从一种新的贝耶斯人在线推断方法中同时推断这些分配变化,并通过整合变化点检测、转换动态系统和巴耶斯人在线学习等理念,使模型适应所检测的变化。 我们用一个二进制的“变化变量”来构建一个信息化的先期,即如果检测到变化,这个模型通过调和来便利适应新的数据分布,部分删除了过去模型更新的信息。 此外,这个方法还利用星际搜索来跟踪多个变化点假设,并在事后观察中选择最可能的选择。 我们提议的方法是模型――不可知性,既适用于受监管的学习环境,也适用于不受监督的学习环境,适合于概念漂移或共变流的环境,并且能够改善古老的巴耶斯人在线学习方法。