A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation

Researchers propose the Minimum Variance Path (MVP) Principle to improve score-based machine learning methods by addressing the path variance problem that makes theoretically path-independent methods practically path-dependent. The approach uses a closed-form variance expression and Kumaraswamy Mixture Model to learn data-adaptive, low-variance paths, achieving new state-of-the-art results on benchmarks.