Quasi Monte Carlo

Quasi-Monte Carlo (QMC) methods utilize deterministically generated, low-discrepancy point sets to improve the efficiency of Monte Carlo integration, particularly in high-dimensional spaces. Current research focuses on applying QMC to accelerate computations in machine learning, including data compression, parameter estimation, and training of neural networks and other models, as well as improving the efficiency of algorithms for solving partial differential equations and graph-based computations. This leads to significant gains in accuracy and speed compared to standard Monte Carlo methods, impacting diverse fields from optimization and data analysis to reinforcement learning and scientific computing. The resulting variance reduction offers substantial improvements in computational efficiency for a wide range of applications.