1 citations · 1 across the 8 of their papers we have counts for
8 papers
Integral-Operator-Based Spectral Algorithms for Goodness-of-Fit Tests
Shiwei Sang, Shao-Bo Lin, Xuehu Zhu
The widespread adoption of the \emph{maximum mean discrepancy} (MMD) in goodness-of-fit testing has spurred extensive research on its statistical performance. However, recent studi…
Feature Qualification by Deep Nets: A Constructive Approach
Feilong Cao, Shao-Bo Lin
The great success of deep learning has stimulated avid research activities in verifying the power of depth in theory, a common consensus of which is that deep net are versatile in…
Component-based Sketching for Deep ReLU Nets
Di Wang, Shao-Bo Lin, Deyu Meng +1
Deep learning has made profound impacts in the domains of data mining and AI, distinguished by the groundbreaking achievements in numerous real-world applications and the innovativ…
An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentation
Ting Yu Tsai, Liangqiao Gui, Yineng Chen +8
Deep learning models have achieved significant success in segmenting cardiovascular structures, but there is a growing need to improve their generalization and robustness. Current…
Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data
Xiaotong Liu, Jinxin Wang, Di Wang +1
Spherical radial-basis-based kernel interpolation abounds in image sciences including geophysical image reconstruction, climate trends description and image rendering due to its ex…
Integral Operator Approaches for Scattered Data Fitting on Spheres
Shao-Bo Lin
This paper focuses on scattered data fitting problems on spheres. We study the approximation performance of a class of weighted spectral filter algorithms, including Tikhonov regul…
Adaptive Parameter Selection for Kernel Ridge Regression
Shao-Bo Lin
This paper focuses on parameter selection issues of kernel ridge regression (KRR). Due to special spectral properties of KRR, we find that delicate subdivision of the parameter int…
Distributed Uncertainty Quantification of Kernel Interpolation on Spheres
Shao-Bo Lin, Xingping Sun, Di Wang
For radial basis function (RBF) kernel interpolation of scattered data, Schaback in 1995 proved that the attainable approximation error and the condition number of the underlying i…