Smooth Density

Smooth density research focuses on understanding and efficiently sampling from probability distributions characterized by continuous and differentiable probability density functions. Current research emphasizes developing and analyzing algorithms, such as diffusion models, neural ordinary differential equations, and Riemannian Langevin algorithms, for learning and sampling from these distributions, often focusing on theoretical guarantees of convergence and sample complexity. This work is crucial for advancing generative modeling, Bayesian inference, and other machine learning applications that rely on accurate and efficient density estimation, particularly in high-dimensional spaces. Improved theoretical understanding and algorithmic efficiency in this area directly impact the performance and reliability of numerous machine learning systems.