37 citations · 76 across the 10 of their papers we have counts for
10 papers
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
Mingcai Chen, Yuntao Du, Wei Tang +4
In real-world applications, perfect labels are rarely available, making it challenging to develop robust machine learning algorithms that can handle noisy labels. Recent methods ha…
Spatial-Temporal Graph Convolutional Gated Recurrent Network for Traffic Forecasting
Le Zhao, Mingcai Chen, Yuntao Du +2
As an important part of intelligent transportation systems, traffic forecasting has attracted tremendous attention from academia and industry. Despite a lot of methods being propos…
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
Mingcai Chen, Hao Cheng, Yuntao Du +3
Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pipeline alternates between eliminating possible incorrect labels and semi-supervis…
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation
Yuntao Du, Haiyang Yang, Mingcai Chen +3
Conventional unsupervised domain adaptation (UDA) methods need to access both labeled source samples and unlabeled target samples simultaneously to train the model. While in some s…
AdaRNN: Adaptive Learning and Forecasting of Time Series
Yuntao Du, Jindong Wang, Wenjie Feng +4
Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes tempo…
Semi-Supervised Learning with Multi-Head Co-Training
Mingcai Chen, Yuntao Du, Yi Zhang +2
Co-training, extended from self-training, is one of the frameworks for semi-supervised learning. Without natural split of features, single-view co-training works at the cost of tra…
Cross-domain error minimization for unsupervised domain adaptation
Yuntao Du, Yinghao Chen, Fengli Cui +2
Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Previous methods focus on learning domain-invariant features t…
Learning transferable and discriminative features for unsupervised domain adaptation
Yuntao Du, Ruiting Zhang, Xiaowen Zhang +3
Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation is able to overcome this cha…
Dual Adversarial Domain Adaptation
Yuntao Du, Zhiwen Tan, Qian Chen +3
Unsupervised domain adaptation aims at transferring knowledge from the labeled source domain to the unlabeled target domain. Previous adversarial domain adaptation methods mostly a…
Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization
Yuntao Du, Zhiwen Tan, Qian Chen +2
Transfer learning has been demonstrated to be successful and essential in diverse applications, which transfers knowledge from related but different source domains to the target do…