91 citations · 154 across the 16 of their papers we have counts for
6 papers · 1 filter
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
Chaochao Chen, Huiwen Wu, Jiajie Su +3
Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-start and data sparsity problem commonly existed in recommender systems. CDR models can improve t…
ASFGNN: Automated Separated-Federated Graph Neural Network
Longfei Zheng, Jun Zhou, Chaochao Chen +3
Graph Neural Networks (GNNs) have achieved remarkable performance by taking advantage of graph data. The success of GNN models always depends on rich features and adjacent relation…
Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors
Li Wang, Leihong Zhang, Chungen Shen +1
We propose a unified framework for multi-view subspace learning to learn individual orthogonal projections for all views. The framework integrates the correlations within multiple…
Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions
Li Wang, Ren-Cang Li, Wen-Wei
We establish a family of subspace-based learning method for multi-view learning using the least squares as the fundamental basis. Specifically, we investigate orthonormalized parti…
Deep Tensor CCA for Multi-view Learning
Hok Shing Wong, Li Wang, Raymond Chan +1
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting…
Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points
Zitong Wang, Li Wang, Raymond Chan +1
We focus on developing a novel scalable graph-based semi-supervised learning (SSL) method for a small number of labeled data and a large amount of unlabeled data. Due to the lack o…