Gradient Oracle

A gradient oracle provides estimates of the gradient of a function, crucial for optimization algorithms in machine learning and other fields. Current research focuses on improving the efficiency and robustness of gradient oracles in various settings, including stochastic, decentralized, and private optimization, often employing variance reduction techniques, primal-dual methods, and adaptive step sizes within algorithms like stochastic gradient descent and its variants. These advancements aim to reduce computational cost, improve convergence rates, and enhance privacy guarantees, impacting the scalability and applicability of optimization methods across diverse applications. The development of efficient and reliable gradient oracles is essential for solving large-scale optimization problems in machine learning and beyond.

Papers