75 citations · 193 across the 22 of their papers we have counts for
10 papers · 1 filter
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples
Suqin Yuan, Lei Feng, Tongliang Liu
Sample selection is a prevalent method in learning with noisy labels, where small-loss data are typically considered as correctly labeled data. However, this method may not effecti…
Exploiting Counter-Examples for Active Learning with Partial labels
Fei Zhang, Yunjie Ye, Lei Feng +6
This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle…
Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples
Shuo He, Lei Feng, Guowu Yang
Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated…
A Universal Unbiased Method for Classification from Aggregate Observations
Zixi Wei, Lei Feng, Bo Han +4
In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances,…
Weakly Supervised Regression with Interval Targets
Xin Cheng, Yuzhou Cao, Ximing Li +2
This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regress…
Partial-Label Regression
Xin Cheng, Deng-Bao Wang, Lei Feng +2
Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial…