148 citations · 279 across the 14 of their papers we have counts for
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cs.LG2023
Improving Heterogeneous Model Reuse by Density Estimation
Anke Tang, Yong Luo, Han Hu +5
This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assum…
cs.LG2023
Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning
Yunke Wang, Bo Du, Chang Xu
Adversarial imitation learning has become a widely used imitation learning framework. The discriminator is often trained by taking expert demonstrations and policy trajectories as…
cs.LG2022★ 3 cited
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks
Chuang Liu, Xueqi Ma, Yibing Zhan +5
Graph Neural Networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and the number of model parameters, which restricts…