4 citations · 13 across the 14 of their papers we have counts for
6 papers · 1 filter
Dual Class-Aware Contrastive Federated Semi-Supervised Learning
Qi Guo, Yong Qi, Saiyu Qi +1
Federated semi-supervised learning (FSSL), facilitates labeled clients and unlabeled clients jointly training a global model without sharing private data. Existing FSSL methods pre…
An Online Sparse Streaming Feature Selection Algorithm
Feilong Chen, Di Wu, Jie Yang +1
Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applica…
DPAUC: Differentially Private AUC Computation in Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on…
Differentially Private AUC Computation in Vertical Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federa…
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data
Di Wu, Peng Zhang, Yi He +1
High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of big data-related applications. Latent factor analysis (LFA) is a typical representation learning method…
Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices
Di Wu, Yi He, Xin Luo
A High-dimensional and sparse (HiDS) matrix is frequently encountered in a big data-related application like an e-commerce system or a social network services system. To perform hi…