# Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data

@inproceedings{Liu2021MetaTT, title={Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data}, author={Feng Liu and Wenkai Xu and Jie Lu and Danica J. Sutherland}, booktitle={NeurIPS}, year={2021} }

Modern kernel-based two-sample tests have shown great success in distinguishing complex, high-dimensional distributions with appropriate learned kernels. Previous work has demonstrated that this kernel learning procedure succeeds, assuming a considerable number of observed samples from each distribution. In realistic scenarios with very limited numbers of data samples, however, it can be challenging to identify a kernel powerful enough to distinguish complex distributions. We address this issue…

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