6 citations · 13 across the 9 of their papers we have counts for
9 papers
Non-Rival Data as Rival Products: An Encapsulation-Forging Approach for Data Synthesis
Kaidong Wang, Jiale Li, Shao-Bo Lin +1
The non-rival nature of data creates a dilemma for firms: sharing data unlocks value but risks eroding competitive advantage. Existing data synthesis methods often exacerbate this…
A Spatio-Temporal Online Robust Tensor Recovery Approach for Streaming Traffic Data Imputation
Yiyang Yang, Xiejian Chi, Shanxing Gao +2
Data quality is critical to Intelligent Transportation Systems (ITS), as complete and accurate traffic data underpin reliable decision-making in traffic control and management. Rec…
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach
Yao Wang, Yiyang Yang, Kaidong Wang +2
We consider the problem of matrix completion with graphs as side information depicting the interrelations between variables. The key challenge lies in leveraging the similarity str…
Tensor Completion Leveraging Graph Information: A Dynamic Regularization Approach with Statistical Guarantees
Kaidong Wang, Qianxin Yi, Yao Wang +3
We consider the problem of tensor completion with graphs serving as side information to represent interrelationships among variables. Existing approaches suffer from several limita…
Efficient Fraud Detection Using Deep Boosting Decision Trees
Biao Xu, Yao Wang, Xiuwu Liao +1
Fraud detection is to identify, monitor, and prevent potentially fraudulent activities from complex data. The recent development and success in AI, especially machine learning, pro…
Effective Streaming Low-tubal-rank Tensor Approximation via Frequent Directions
Qianxin Yi, Chenhao Wang, Kaidong Wang +1
Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multi-dimensional data. However, finding such an accurate approximation is challenging in the strea…
Universal Consistency of Deep Convolutional Neural Networks
Shao-Bo Lin, Kaidong Wang, Yao Wang +1
Compared with avid research activities of deep convolutional neural networks (DCNNs) in practice, the study of theoretical behaviors of DCNNs lags heavily behind. In particular, th…
Understanding Self-Paced Learning under Concave Conjugacy Theory
Shiqi Liu, Zilu Ma, Deyu Meng
By simulating the easy-to-hard learning manners of humans/animals, the learning regimes called curriculum learning~(CL) and self-paced learning~(SPL) have been recently investigate…
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning
Kaidong Wang, Yao Wang, Qian Zhao +2
It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the tradi…