3 citations · 9 across the 14 of their papers we have counts for
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cs.LG2021★ 1 cited
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Maoguo Gong, Yuan Gao, Yue Wu +1
Distributed multi-party learning provides an effective approach for training a joint model with scattered data under legal and practical constraints. However, due to the quagmire o…
cs.LG2021★ 1 cited
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
Xiaolong Fan, Maoguo Gong, Yue Wu +1
Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture inf…
cs.LG2021
Multi-Party Dual Learning
Maoguo Gong, Yuan Gao, Yu Xie +3
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed partie…