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FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning


Jun 07, 2022
Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Zhongliang Yang, Yongfeng Huang, Xing Xie


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Robust Quantity-Aware Aggregation for Federated Learning


May 22, 2022
Jingwei Yi, Fangzhao Wu, Huishuai Zhang, Bin Zhu, Tao Qi, Guangzhong Sun, Xing Xie


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FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation


Apr 21, 2022
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie


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FUM: Fine-grained and Fast User Modeling for News Recommendation


Apr 10, 2022
Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang

* SIGIR 2022 

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News Recommendation with Candidate-aware User Modeling


Apr 10, 2022
Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang

* SIGIR 2022 

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ProFairRec: Provider Fairness-aware News Recommendation


Apr 10, 2022
Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, Xing Xie

* SIGIR 2022 

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Unified and Effective Ensemble Knowledge Distillation


Apr 01, 2022
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang


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FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation


Apr 01, 2022
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang


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End-to-end Learnable Diversity-aware News Recommendation


Apr 01, 2022
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang


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Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder


Apr 01, 2022
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang


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