13 citations · 15 across the 13 of their papers we have counts for
9 papers · 1 filter
Learning manifold diffusion semigroups from graph transition matrices
Xiuyuan Cheng, Nan Wu
We consider graph diffusion processes constructed from finite i.i.d. samples drawn from an unknown manifold embedded in ambient Euclidean space, where the graph affinity is defined…
Worst-case generation via minimax optimization in Wasserstein space
Xiuyuan Cheng, Yao Xie, Linglingzhi Zhu +1
Worst-case generation plays a critical role in evaluating robustness and stress-testing systems under distribution shifts, in applications ranging from machine learning models to p…
High-dimensional Mean-Field Games by Particle-based Flow Matching
Jiajia Yu, Junghwan Lee, Yao Xie +1
Mean-field games (MFGs) study the Nash equilibrium of systems with a continuum of interacting agents, which can be formulated as the fixed-point of optimal control problems. They p…
Deep spatio-temporal point processes: Advances and new directions
Xiuyuan Cheng, Zheng Dong, Yao Xie
Spatio-temporal point processes (STPPs) model discrete events distributed in time and space, with important applications in areas such as criminology, seismology, epidemiology, and…
Point processes with event time uncertainty
Xiuyuan Cheng, Tingnan Gong, Yao Xie
Point processes are widely used statistical models for continuous-time discrete event data, such as medical records, crime reports, and social network interactions, to capture the…
Posterior sampling via Langevin dynamics based on generative priors
Vishal Purohit, Matthew Repasky, Jianfeng Lu +3
Posterior sampling in high-dimensional spaces using generative models holds significant promise for various applications, including but not limited to inverse problems and guided g…
Local Flow Matching Generative Models
Chen Xu, Xiuyuan Cheng, Yao Xie
Flow Matching (FM) is a simulation-free method for learning a continuous, invertible flow that interpolates between two distributions, and in particular generates data from noise.…
Training Guarantees of Neural Network Classification Two-Sample Tests by Kernel Analysis
Varun Khurana, Xiuyuan Cheng, Alexander Cloninger
We construct and analyze a neural network two-sample test to determine whether two datasets came from the same distribution (null hypothesis) or not (alternative hypothesis). We pe…
Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
Xiuyuan Cheng, Jianfeng Lu, Yixin Tan +1
Flow-based generative models enjoy certain advantages in computing the data generation and the likelihood, and have recently shown competitive empirical performance. Compared to th…