Adapting to Covariate Shift in Real-time by Encoding Trees with Motion
  Equations
Tham Yik Foong Heng Zhang Mao Po Yuan Danilo Vasconcellos Vargas
Input distribution shift presents a significant problem in many real-world
systems. Here we present Xenovert, an adaptive algorithm that can dynamically
adapt to changes in input distribution. It is a perfect binary tree that
adaptively divides a continuous input space into several intervals of uniform
density while receiving a continuous stream of input. This process indirectly
maps the source distribution to the shifted target distribution, preserving the
data's relationship with the downstream decoder/operation, even after the shift
occurs. In this paper, we demonstrated how a neural network integrated with
Xenovert achieved better results in 4 out of 5 shifted datasets, saving the
hurdle of retraining a machine learning model. We anticipate that Xenovert can
be applied to many more applications that require adaptation to unforeseen
input distribution shifts, even when the distribution shift is drastic.