7 papers
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.…
Online Kernel CUSUM for Change-Point Detection
Song Wei, Yao Xie
We present a computationally efficient online kernel Cumulative Sum (CUSUM) method for change-point detection that utilizes the maximum over a set of kernel statistics to account f…
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…
Flow-based generative models as iterative algorithms in probability space
Yao Xie, Xiuyuan Cheng
Generative AI (GenAI) has revolutionized data-driven modeling by enabling the synthesis of high-dimensional data across various applications, including image generation, language m…
Computing high-dimensional optimal transport by flow neural networks
Chen Xu, Xiuyuan Cheng, Yao Xie
Computing optimal transport (OT) for general high-dimensional data has been a long-standing challenge. Despite much progress, most of the efforts including neural network methods h…