Learning Probability Distribution

Learning probability distributions focuses on developing methods to accurately model and generate data from complex, often high-dimensional, probability distributions. Current research emphasizes improving the efficiency and robustness of existing techniques like normalizing flows and score-based generative models, as well as exploring novel approaches such as learning sparse representations in large language models and leveraging distributional information in reinforcement learning. These advancements have significant implications for diverse fields, including machine learning, robotics, and healthcare, by enabling more accurate modeling of real-world phenomena and improved decision-making under uncertainty.