activity
20172022
most citedNeural Tangent Kernel Maximum Mean Discrepancy

4 citations · 14 across the 7 of their papers we have counts for

collaborators

17 papers

cs.LG20221 cited

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…

stat.ML20214 cited

Neural Tangent Kernel Maximum Mean Discrepancy

Xiuyuan Cheng, Yao Xie

We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by identifying a new connection between neural tangent kernel (NTK) and MMD. This connection enables us t…

cs.LG20211 cited

Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples

Yixing Zhang, Xiuyuan Cheng, Galen Reeves

The Gaussian-smoothed optimal transport (GOT) framework, recently proposed by Goldfeld et al., scales to high dimensions in estimation and provides an alternative to entropy regula…

math.ST20203 cited

Convergence of Graph Laplacian with kNN Self-tuned Kernels

Xiuyuan Cheng, Hau-Tieng Wu

Kernelized Gram matrix constructed from data points as is widely used in graph-based geometric data analysis…

cs.CV20203 cited

ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution

Ze Wang, Xiuyuan Cheng, Guillermo Sapiro +1

Convolutional Neural Networks (CNNs) are known to be significantly over-parametrized, and difficult to interpret, train and adapt. In this paper, we introduce a structural regulari…

stat.ML2020

Graph Convolution with Low-rank Learnable Local Filters

Xiuyuan Cheng, Zichen Miao, Qiang Qiu

Geometric variations like rotation, scaling, and viewpoint changes pose a significant challenge to visual understanding. One common solution is to directly model certain intrinsic…