Adapting to Covariate Shift in Real-time by Encoding Trees with Motion Equations
This addresses the challenge of covariate shift for applications requiring real-time adaptation, though it appears incremental as it builds on existing adaptive methods.
The paper tackles the problem of input distribution shift in real-world systems by introducing Xenovert, an adaptive algorithm that dynamically adjusts to changes in input distribution, achieving better results in 4 out of 5 shifted datasets without retraining.
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.