19 citations · 23 across the 14 of their papers we have counts for
7 papers · 1 filter
CALM: Consensus-Aware Localized Merging for Multi-Task Learning
Kunda Yan, Min Zhang, Sen Cui +4
Model merging aims to integrate the strengths of multiple fine-tuned models into a unified model while preserving task-specific capabilities. Existing methods, represented by task…
Balancing Similarity and Complementarity for Federated Learning
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi +5
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively using data while maintaining user privacy. One key challenge in FL is managing statisti…
Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment
Sen Cui, Weishen Pan, Changshui Zhang +1
Algorithmic fairness has been a serious concern and received lots of interest in machine learning community. In this paper, we focus on the bipartite ranking scenario, where the in…
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition
Weishen Pan, Sen Cui, Jiang Bian +2
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the devel…
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…
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
Sen Cui, Weishen Pan, Changshui Zhang +1
Bipartite ranking, which aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data, is widely adopted in various applications whe…