454 citations · 2.5k across the 132 of their papers we have counts for
16 papers · 1 filter
Learning on Attribute-Missing Graphs
Xu Chen, Siheng Chen, Jiangchao Yao +3
Graphs with complete node attributes have been widely explored recently. While in practice, there is a graph where attributes of only partial nodes could be available and those of…
Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation
Yan Zhang, Ivor W. Tsang, Lixin Duan
Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce. Most existing studies proposed to alleviate the cold-start problem are also k…
Deep Pairwise Hashing for Cold-start Recommendation
Yan Zhang, Ivor W. Tsang, Hongzhi Yin +3
Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focu…
The Emerging Trends of Multi-Label Learning
Weiwei Liu, Haobo Wang, Xiaobo Shen +1
Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For e…
A Survey of Label-noise Representation Learning: Past, Present and Future
Bo Han, Quanming Yao, Tongliang Liu +4
Classical machine learning implicitly assumes that labels of the training data are sampled from a clean distribution, which can be too restrictive for real-world scenarios. However…
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo
Yueming Lyu, Yuan Yuan, Ivor W. Tsang
Quasi-Monte Carlo (QMC) is an essential tool for integral approximation, Bayesian inference, and sampling for simulation in science, etc. In the QMC area, the rank-1 lattice is imp…
Graph Cross Networks with Vertex Infomax Pooling
Maosen Li, Siheng Chen, Ya Zhang +1
We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph. Based on trainable hierarchical representations of a graph,…
Deep N-ary Error Correcting Output Codes
Hao Zhang, Joey Tianyi Zhou, Tianying Wang +2
Ensemble learning consistently improves the performance of multi-class classification through aggregating a series of base classifiers. To this end, data-independent ensemble metho…
Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation
Xu Chen, Ya Zhang, Ivor Tsang +2
Cross domain recommendation (CDR) is one popular research topic in recommender systems. This paper focuses on a popular scenario for CDR where different domains share the same set…
Learning Node Representations against Perturbations
Xu Chen, Yuangang Pan, Ivor Tsang +1
Recent graph neural networks (GNN) has achieved remarkable performance in node representation learning. One key factor of GNN's success is the \emph{smoothness} property on node re…
Intrinsic Reward Driven Imitation Learning via Generative Model
Xingrui Yu, Yueming Lyu, Ivor W. Tsang
Imitation learning in a high-dimensional environment is challenging. Most inverse reinforcement learning (IRL) methods fail to outperform the demonstrator in such a high-dimensiona…
Multi-view Alignment and Generation in CCA via Consistent Latent Encoding
Yaxin Shi, Yuangang Pan, Donna Xu +1
Multi-view alignment, achieving one-to-one correspondence of multi-view inputs, is critical in many real-world multi-view applications, especially for cross-view data analysis prob…
Secure Metric Learning via Differential Pairwise Privacy
Jing Li, Yuangang Pan, Yulei Sui +1
Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of indi…
Copy and Paste GAN: Face Hallucination from Shaded Thumbnails
Yang Zhang, Ivor Tsang, Yawei Luo +3
Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination conditio…
Face Hallucination with Finishing Touches
Yang Zhang, Ivor W. Tsang, Jun Li +3
Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams eit…
Towards Sharper First-Order Adversary with Quantized Gradients
Zhuanghua Liu, Ivor W. Tsang
Despite the huge success of Deep Neural Networks (DNNs) in a wide spectrum of machine learning and data mining tasks, recent research shows that this powerful tool is susceptible t…