  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.
