DIstribution Estimation

Distribution estimation focuses on accurately determining the probability distribution of data, a crucial task across numerous scientific fields and applications. Current research emphasizes robust methods that handle noisy, incomplete, or adversarially corrupted data, often employing techniques like diffusion models, generative adversarial networks (GANs), and Wasserstein distance-based approaches. These advancements are improving the reliability and efficiency of distribution estimation in diverse areas, including reinforcement learning, federated learning, and natural language processing, leading to more accurate models and better decision-making in complex systems.