论文标题
检测和适应贝叶斯在线学习的不规则分配变化
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
论文作者
论文摘要
我们考虑以未知率和未知强度发生的分配变化存在的在线学习问题。我们得出了一种新的贝叶斯在线推理方法，可以同时推断这些分布变化，并通过从变化点检测，切换动力学系统和贝叶斯在线学习中整合想法来使模型适应检测到的变化。使用二进制“变量变量”，我们构建了一个信息性的事先，即如果检测到更改 - 模型通过缓和以促进对新数据分布的适应来部分删除过去模型更新的信息。此外，该方法使用光束搜索来跟踪多个更改的假设，并在事后选择最可能的假设。我们提出的方法是模型不合时宜的，适用于受监督和无监督的学习设置，适用于概念漂移环境或协变量漂移环境，并且对最先进的贝叶斯在线学习方法进行了改善。
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.
