Gradient Estimation

Gradient estimation focuses on efficiently approximating gradients of objective functions, particularly in scenarios where direct computation is infeasible or computationally expensive, such as those involving high-dimensional data, stochasticity, or discrete decision-making. Current research emphasizes developing variance-reduced estimators, exploring decentralized and federated learning settings, and adapting gradient estimation to various model architectures including neural networks, regression trees, and quantum circuits. These advancements are crucial for improving the efficiency and scalability of optimization algorithms across diverse fields, ranging from machine learning and reinforcement learning to robotics and high-energy physics.