5 citations · 15 across the 11 of their papers we have counts for
5 papers · 1 filter
Semi-Supervised Conditional Diffusion via Label Augmentation
Jin Su, Yuan Gao, Yong Zhou +1
Conditional diffusion models have become a powerful and flexible framework for learning complex conditional distributions from labeled data. In practice, however, acquiring high-qu…
From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps
Chenxuan He, Yuan Gao, Liping Zhu +1
Testing conditional independence between two random vectors given a third is a fundamental and challenging problem in statistics, particularly in multivariate nonparametric setting…
Convergence of Continuous Normalizing Flows for Learning Probability Distributions
Yuan Gao, Jian Huang, Yuling Jiao +1
Continuous normalizing flows (CNFs) are a generative method for learning probability distributions, which is based on ordinary differential equations. This method has shown remarka…
Gaussian Interpolation Flows
Yuan Gao, Jian Huang, Yuling Jiao
Gaussian denoising has emerged as a powerful method for constructing simulation-free continuous normalizing flows for generative modeling. Despite their empirical successes, theore…
Relative Entropy Gradient Sampler for Unnormalized Distributions
Xingdong Feng, Yuan Gao, Jian Huang +2
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms i…