52 citations · 74 across the 12 of their papers we have counts for
12 papers
Regularly Truncated M-estimators for Learning with Noisy Labels
Xiaobo Xia, Pengqian Lu, Chen Gong +3
The sample selection approach is very popular in learning with noisy labels. As deep networks learn pattern first, prior methods built on sample selection share a similar training…
Leverage classifier: Another look at support vector machine
Yixin Han, Jun Yu, Nan Zhang +4
Support vector machine (SVM) is a popular classifier known for accuracy, flexibility, and robustness. However, its intensive computation has hindered its application to large-scale…
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels
Hui Kang, Sheng Liu, Huaxi Huang +4
In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean da…
Importance Sparsification for Sinkhorn Algorithm
Mengyu Li, Jun Yu, Tao Li +1
Sinkhorn algorithm has been used pervasively to approximate the solution to optimal transport (OT) and unbalanced optimal transport (UOT) problems. However, its practical applicati…
A reinforced learning approach to optimal design under model uncertainty
Mingyao Ai, Holger Dette, Zhengfu Liu +1
Optimal designs are usually model-dependent and likely to be sub-optimal if the postulated model is not correctly specified. In practice, it is common that a researcher has a list…
Core-Elements for Large-Scale Least Squares Estimation
Mengyu Li, Jun Yu, Tao Li +1
The coresets approach, also called subsampling or subset selection, aims to select a subsample as a surrogate for the observed sample and has found extensive applications in large-…
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation
Jingyi Zhang, Cheng Meng, Jun Yu +3
Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and pr…
Hilbert Curve Projection Distance for Distribution Comparison
Tao Li, Cheng Meng, Hongteng Xu +1
Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbe…
Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification
Mengyu Li, Jun Yu, Hongteng Xu +1
As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs. However,…
Smoothing splines approximation using Hilbert curve basis selection
Cheng Meng, Jun Yu, Yongkai Chen +2
Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size is large.…
Sufficient dimension reduction for classification using principal optimal transport direction
Cheng Meng, Jun Yu, Jingyi Zhang +2
Sufficient dimension reduction is used pervasively as a supervised dimension reduction approach. Most existing sufficient dimension reduction methods are developed for data with a…
Optimal Subsampling Algorithms for Big Data Regressions
Mingyao Ai, Jun Yu, Huiming Zhang +1
To fast approximate maximum likelihood estimators with massive data, this paper studies the Optimal Subsampling Method under the A-optimality Criterion (OSMAC) for generalized line…