Neural Ratio Estimation

Neural ratio estimation (NRE) is a machine learning approach to solving inverse problems where the likelihood function is intractable, a common challenge in many scientific fields. Current research focuses on improving the accuracy and reliability of NRE methods, including developing new architectures like balanced NRE to mitigate overconfidence and exploring alternative divergence measures beyond the standard Kullback-Leibler divergence. These advancements enhance the efficiency and trustworthiness of inference, particularly in high-dimensional problems, with applications ranging from cosmology to engineering design. The resulting improvements in computational efficiency and inferential accuracy are significant for fields relying on complex simulations.