Two Sample

Two-sample testing aims to determine whether two datasets originate from the same underlying distribution. Current research focuses on developing efficient and powerful tests, particularly for high-dimensional data and scenarios with censored or mismeasured observations, employing methods such as kernel-based tests (e.g., Maximum Mean Discrepancy), neural networks, and novel combinations of classical and machine learning approaches. These advancements are crucial for various scientific fields, enabling robust comparisons of datasets in diverse applications ranging from particle physics and neuroscience to medical image analysis and reinforcement learning. Improved methods enhance the reliability of scientific inferences and facilitate more accurate model validation.