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…
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…