Low Variance Gradient

Low-variance gradient estimation focuses on developing efficient methods for approximating gradients of objective functions, particularly in scenarios with high dimensionality, noisy data, or computationally expensive evaluations. Current research emphasizes variance reduction techniques within stochastic gradient descent, incorporating second-order information for improved robustness and scalability, and exploring alternative estimators like those based on evolution strategies, particularly for handling complex or black-box functions. These advancements are crucial for accelerating optimization in various machine learning applications, including variational inference, reinforcement learning, and solving inverse problems, leading to more efficient and robust algorithms.