Variational Objective

Variational objectives are mathematical functions used to approximate intractable probability distributions in machine learning, primarily aiming to find optimal parameters for models by minimizing the difference between a simplified approximation and the true distribution. Current research focuses on improving the efficiency and accuracy of these approximations, exploring techniques like variance reduction, programmable inference frameworks, and novel gradient estimators within various model architectures such as variational autoencoders, diffusion models, and mixtures of experts. These advancements are significant for improving the scalability and performance of probabilistic models across diverse applications, including generative modeling, causal inference, and reinforcement learning.