Efficient Sampling

Efficient sampling aims to generate representative data subsets from complex distributions, accelerating computations and improving the accuracy of various analyses. Current research focuses on developing novel sampling algorithms, including those based on Markov Chain Monte Carlo (MCMC), diffusion models, and ordinary differential equations (ODEs), to address challenges in high-dimensional spaces and specific data structures like graphs and manifolds. These advancements are crucial for improving the efficiency and scalability of machine learning, statistical inference, and scientific simulations across diverse fields, ranging from generative modeling to hypothesis testing and rare-event analysis. The development of more efficient sampling techniques directly impacts the feasibility and cost-effectiveness of numerous computational tasks.