Neural Tangent Kernel Maximum Mean Discrepancy
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.
Duke Scholars
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Related Subject Headings
- 4611 Machine learning
- 1702 Cognitive Sciences
- 1701 Psychology
Citation
Published In
ISSN
ISBN
Publication Date
Volume
Start / End Page
Related Subject Headings
- 4611 Machine learning
- 1702 Cognitive Sciences
- 1701 Psychology