Gradient Estimator

Gradient estimators are computational methods used to approximate gradients of objective functions, particularly in scenarios where direct calculation is intractable, such as those involving discrete variables or stochastic models. Current research focuses on improving the efficiency and accuracy of these estimators, addressing issues like high variance and bias through techniques such as variance reduction, control variates, and adaptive methods. These advancements are crucial for optimizing complex machine learning models, including normalizing flows, variational autoencoders, and those used in federated learning and reinforcement learning, ultimately impacting the scalability and performance of these applications.