Adapting to Covariate Shift in Real-time by Encoding Trees with Motion Equations
<|reference_start|>Adapting to Covariate Shift in Real-time by Encoding Trees with Motion Equations: 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.<|reference_end|>
@article{foong2024adapting,
    title={Adapting to Covariate Shift in Real-time by Encoding Trees with Motion
  Equations},
    author={Tham Yik Foong, Heng Zhang, Mao Po Yuan, Danilo Vasconcellos Vargas},
    journal={arXiv preprint arXiv:2404.05168},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05168},
    primaryClass={cs.LG}
}