Distribution Matching

Distribution matching, a core machine learning problem, aims to align two probability distributions, enabling robust knowledge transfer and data synthesis. Current research focuses on developing efficient algorithms, such as those based on optimal transport (e.g., Wasserstein distance) and adversarial networks, to achieve this alignment, often addressing challenges like computational complexity and instability inherent in existing methods. These advancements have significant implications for diverse applications, including generative modeling, domain adaptation, and multi-task learning, by improving model performance and generalization capabilities. Furthermore, research explores partial distribution matching to handle incomplete or imbalanced data, enhancing robustness and efficiency.