52 citations · 74 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
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,…
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…