39 citations · 221 across the 21 of their papers we have counts for
5 papers · 1 filter
Incentive Compatible Pareto Alignment for Multi-Source Large Graphs
Jian Liang, Fangrui Lv, Di Liu +5
In this paper, we focus on learning effective entity matching models over multi-source large-scale data. For real applications, we relax typical assumptions that data distributions…
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
Sen Cui, Weishen Pan, Jian Liang +2
Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples acro…
Collaboration Equilibrium in Federated Learning
Sen Cui, Jian Liang, Weishen Pan +3
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy. Recently, there have…
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Mi Luo, Fei Chen, Dapeng Hu +3
A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforc…
On Evolving Attention Towards Domain Adaptation
Kekai Sheng, Ke Li, Xiawu Zheng +5
Towards better unsupervised domain adaptation (UDA). Recently, researchers propose various domain-conditioned attention modules and make promising progresses. However, considering…