Stochastic Search

Stochastic search encompasses a broad range of algorithms designed to efficiently explore complex search spaces, often in the presence of noise or uncertainty, to find optimal or near-optimal solutions. Current research focuses on improving the convergence rates and efficiency of these algorithms, particularly within the contexts of machine learning (e.g., gradient-based methods for risk minimization and neural network quantization), optimization (e.g., Monte Carlo Tree Search and direct search methods), and resource allocation problems. These advancements have significant implications for diverse fields, enabling more efficient solutions to challenging problems in areas such as satellite scheduling, advertising optimization, and brain-imaging analysis.