5 citations · 15 across the 11 of their papers we have counts for
11 papers
Sampling and Optimization meet Enhanced Flows
Yuan Gao, Siming He, Eitan Tadmor
It is well known that the computational realization of Gibbs probability measures, , plays a central role in sampling and optimization. In this paper…
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…
Lipschitz Transport Maps via the Follmer Flow
Yin Dai, Yuan Gao, Jian Huang +3
Inspired by the construction of the F{ö}llmer process, we construct a unit-time flow on the Euclidean space, termed the F{ö}llmer flow, whose flow map at time 1 pushes forward a st…
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…
Generative Learning With Euler Particle Transport
Yuan Gao, Jian Huang, Yuling Jiao +3
We propose an Euler particle transport (EPT) approach for generative learning. The proposed approach is motivated by the problem of finding an optimal transport map from a referenc…
Learning Implicit Generative Models with Theoretical Guarantees
Yuan Gao, Jian Huang, Yuling Jiao +1
We propose a \textbf{uni}fied \textbf{f}ramework for \textbf{i}mplicit \textbf{ge}nerative \textbf{m}odeling (UnifiGem) with theoretical guarantees by integrating approaches from o…
Wasserstein-Wasserstein Auto-Encoders
Shunkang Zhang, Yuan Gao, Yuling Jiao +3
To address the challenges in learning deep generative models (e.g.,the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we pr…
Deep Generative Learning via Variational Gradient Flow
Yuan Gao, Yuling Jiao, Yang Wang +3
We propose a general framework to learn deep generative models via \textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability spaces. The evolving distribution tha…