Unbiased Sampling

Unbiased sampling aims to generate representative data samples from complex probability distributions, crucial for tasks like training machine learning models and simulating physical systems. Current research focuses on improving the efficiency and accuracy of unbiased sampling using various approaches, including diffusion models, flow-based generative models, and importance sampling techniques, often applied within the context of Boltzmann distributions or reinforcement learning. These advancements are significant because unbiased sampling enables more robust and efficient training of machine learning models, leading to improved performance in diverse applications, and allows for more accurate simulations in scientific computing.