Kernel Two Sample Test

Kernel two-sample tests are statistical methods used to determine whether two datasets are drawn from the same underlying distribution, particularly useful for high-dimensional or complex data where traditional methods struggle. Current research focuses on improving the computational efficiency of these tests, particularly by employing random Fourier features and addressing robustness to data corruption or adversarial attacks, often within a minimax optimality framework. These advancements are significant because they enable the application of powerful non-parametric tests to large datasets and real-world scenarios, impacting fields ranging from causal inference and time-series analysis to multimodal data classification and robust hypothesis testing.