6 citations · 12 across the 2 of their papers we have counts for
5 papers
Approximating Instance-Dependent Noise via Instance-Confidence Embedding
Yivan Zhang, Masashi Sugiyama
Label noise in multiclass classification is a major obstacle to the deployment of learning systems. However, unlike the widely used class-conditional noise (CCN) assumption that th…
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
Yivan Zhang, Gang Niu, Masashi Sugiyama
Many weakly supervised classification methods employ a noise transition matrix to capture the class-conditional label corruption. To estimate the transition matrix from noisy data,…
Classification with Rejection Based on Cost-sensitive Classification
Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang +1
The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection. In this paper, based…
Learning from Aggregate Observations
Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu +1
We study the problem of learning from aggregate observations where supervision signals are given to sets of instances instead of individual instances, while the goal is still to pr…
Learning from Indirect Observations
Yivan Zhang, Nontawat Charoenphakdee, Masashi Sugiyama
Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals. While existing work mainly f…