Neural Tangent Kernel Maximum Mean Discrepancy
Abstract
We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by identifying a new connection between neural tangent kernel (NTK) and MMD. This connection enables us to develop a computationally efficient and memory-efficient approach to compute the MMD statistic and perform NTK based two-sample tests towards addressing the long-standing challenge of memory and computational complexity of the MMD statistic, which is essential for online implementation to assimilating new samples. Theoretically, such a connection allows us to understand the NTK test statistic properties, such as the Type-I error and testing power for performing the two-sample test, by adapting existing theories for kernel MMD. Numerical experiments on synthetic and real-world datasets validate the theory and demonstrate the effectiveness of the proposed NTK-MMD statistic.
Keywords
Cite
@article{arxiv.2106.03227,
title = {Neural Tangent Kernel Maximum Mean Discrepancy},
author = {Xiuyuan Cheng and Yao Xie},
journal= {arXiv preprint arXiv:2106.03227},
year = {2021}
}