454 citations · 674 across the 15 of their papers we have counts for
15 papers · 1 filter
Complementary to Multiple Labels: A Correlation-Aware Correction Approach
Yi Gao, Miao Xu, Min-Ling Zhang
\textit{Complementary label learning} (CLL) requires annotators to give \emph{irrelevant} labels instead of relevant labels for instances. Currently, CLL has shown its promising pe…
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
Jonathan Wilton, Abigail M. Y. Koay, Ryan K. L. Ko +2
The need to learn from positive and unlabeled data, or PU learning, arises in many applications and has attracted increasing interest. While random forests are known to perform wel…
A Boosting Algorithm for Positive-Unlabeled Learning
Yawen Zhao, Mingzhe Zhang, Chenhao Zhang +3
Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. Many recent PU methods are based on neural n…
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
Cheng-Yu Hsieh, Wei-I Lin, Miao Xu +3
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where t…
On the Robustness of Average Losses for Partial-Label Learning
Jiaqi Lv, Biao Liu, Lei Feng +6
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (I…
Pointwise Binary Classification with Pairwise Confidence Comparisons
Lei Feng, Senlin Shu, Nan Lu +5
To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed. Among them, some…
Provably Consistent Partial-Label Learning
Lei Feng, Jiaqi Lv, Bo Han +5
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL metho…
Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv, Miao Xu, Lei Feng +3
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the tr…
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative
Miao Xu, Bingcong Li, Gang Niu +2
In the early history of positive-unlabeled (PU) learning, the sample selection approach, which heuristically selects negative (N) data from U data, was explored extensively. Howeve…
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Bo Han, Gang Niu, Xingrui Yu +4
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end. Although equipped with corrections for noisy labels,…
Clipped Matrix Completion: A Remedy for Ceiling Effects
Takeshi Teshima, Miao Xu, Issei Sato +1
We consider the problem of recovering a low-rank matrix from its clipped observations. Clipping is conceivable in many scientific areas that obstructs statistical analyses. On the…
Matrix Co-completion for Multi-label Classification with Missing Features and Labels
Miao Xu, Gang Niu, Bo Han +3
We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries. An existing method concatenated $\X$ and $\Y…
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han, Quanming Yao, Xingrui Yu +5
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during train…
Active Feature Acquisition with Supervised Matrix Completion
Sheng-Jun Huang, Miao Xu, Ming-Kun Xie +3
Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance. While feature…
CUR Algorithm for Partially Observed Matrices
Miao Xu, Rong Jin, Zhi-Hua Zhou
CUR matrix decomposition computes the low rank approximation of a given matrix by using the actual rows and columns of the matrix. It has been a very useful tool for handling large…