256 citations · 713 across the 15 of their papers we have counts for
15 papers
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning
Yanjun Zhang, Guangdong Bai, Xue Li +2
Federated learning enables multiple participants to collaboratively train a model without aggregating the training data. Although the training data are kept within each participant…
Learning Causal Bayesian Networks from Text
Farhad Moghimifar, Afshin Rahimi, Mahsa Baktashmotlagh +1
Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic…
A Unified Model for Recommendation with Selective Neighborhood Modeling
Jingwei Ma, Jiahui Wen, Panpan Zhang +2
Neighborhood-based recommenders are a major class of Collaborative Filtering (CF) models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-it…
PrivColl: Practical Privacy-Preserving Collaborative Machine Learning
Yanjun Zhang, Guangdong Bai, Xue Li +3
Collaborative learning enables two or more participants, each with their own training dataset, to collaboratively learn a joint model. It is desirable that the collaboration should…
Learning Various Length Dependence by Dual Recurrent Neural Networks
Chenpeng Zhang, Shuai Li, Mao Ye +2
Recurrent neural networks (RNNs) are widely used as a memory model for sequence-related problems. Many variants of RNN have been proposed to solve the gradient problems of training…
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning
Shaoxiong Ji, Wenqi Jiang, Anwar Walid +1
Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. Deep neural networks' rapid d…