13 citations · 23 across the 13 of their papers we have counts for
13 papers
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…
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.…
Flow-based Distributionally Robust Optimization
Chen Xu, Jonghyeok Lee, Xiuyuan Cheng +1
We present a computationally efficient framework, called , for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty…
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…
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…
Spatio-temporal point processes with deep non-stationary kernels
Zheng Dong, Xiuyuan Cheng, Yao Xie
Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent n…
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…
Neural network-based CUSUM for online change-point detection
Tingnan Gong, Junghwan Lee, Xiuyuan Cheng +1
Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning. CUSUM is a popular st…
Crime Hot-Spot Modeling via Topic Modeling and Relative Density Estimation
Jonathan Zhou, Sarah Huestis-Mitchell, Xiuyuan Cheng +1
We present a method to capture groupings of similar calls and determine their relative spatial distribution from a collection of crime record narratives. We first obtain a topic di…
Kernel Two-Sample Tests for Manifold Data
Xiuyuan Cheng, Yao Xie
We present a study of a kernel-based two-sample test statistic related to the Maximum Mean Discrepancy (MMD) in the manifold data setting, assuming that high-dimensional observatio…