Self-normalized processes arise naturally in many statistical tasks. While self-normalized
concentration has been extensively studied for scalar-valued processes, there is less work …

We propose novel statistics which maximise the power of a two-sample test based on the
Maximum Mean Discrepancy (MMD), byadapting over the set of kernels used in defining it …

The average treatment effect, which is the difference in expectation of the counterfactuals, is
probably the most popular target effect in causal inference with binary treatments. However …

Two-sample tests are important in statistics and machine learning, both as tools for scientific
discovery as well as to detect distribution shifts. This led to the development of many …

In nonparametric independence testing, we observe iid data {(Xi, Yi)} ni= 1, where X∈ Χ,
Y∈ Y lie in any general spaces, and we wish to test the null that X is independent of Y …

Recent years have witnessed increasing attentions on two-sample test with diverse real
applications, while this work takes one more step on the exploration of local significant …

Recent years have witnessed growing concerns about the privacy of sensitive data. In
response to these concerns, differential privacy has emerged as a rigorous framework for …

The kernel two-sample test based on the maximum mean discrepancy is one of the most
popular methods for detecting differences between two distributions over general metric …

We prove a convergence theorem for U-statistics of degree two, where the data dimension $
d $ is allowed to scale with sample size $ n $. We find that the limiting distribution of a U …

Additional results are provided in the supplementary material [43]. Appendix A discusses
multiple sample-splitting, while Appendix B describes a general strategy for studying the …