Zobrazeno 1 - 10
of 292
pro vyhledávání: '"Chen, CHaochao"'
Autor:
Han, Zhongxuan, Zhang, Li, Chen, Chaochao, Zheng, Xiaolin, Zheng, Fei, Li, Yuyuan, Yin, Jianwei
Federated Learning (FL) employs a training approach to address scenarios where users' data cannot be shared across clients. Achieving fairness in FL is imperative since training data in FL is inherently geographically distributed among diverse user g
Externí odkaz:
http://arxiv.org/abs/2411.06881
Autor:
Liao, Xinting, Liu, Weiming, Zhou, Pengyang, Yu, Fengyuan, Xu, Jiahe, Wang, Jun, Wang, Wenjie, Chen, Chaochao, Zheng, Xiaolin
Federated learning (FL) is a promising machine learning paradigm that collaborates with client models to capture global knowledge. However, deploying FL models in real-world scenarios remains unreliable due to the coexistence of in-distribution data
Externí odkaz:
http://arxiv.org/abs/2410.11397
Session-based Recommendation (SBR), seeking to predict a user's next action based on an anonymous session, has drawn increasing attention for its practicability. Most SBR models only rely on the contextual transitions within a short session to learn
Externí odkaz:
http://arxiv.org/abs/2410.10296
Autor:
Yang, Ziqi, Peng, Zhaopeng, Wang, Zihui, Qi, Jianzhong, Chen, Chaochao, Pan, Weike, Wen, Chenglu, Wang, Cheng, Fan, Xiaoliang
Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of
Externí odkaz:
http://arxiv.org/abs/2410.08249
Autor:
Huang, Heyuan, Lou, Xingyu, Chen, Chaochao, Cheng, Pengxiang, Xin, Yue, He, Chengwei, Liu, Xiang, Wang, Jun
Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an ideal static condition that is not practical in industrial recommendati
Externí odkaz:
http://arxiv.org/abs/2410.10835
Autor:
Chen, Chaochao, Zhang, Jiaming, Zhang, Yizhao, Zhang, Li, Lyu, Lingjuan, Li, Yuyuan, Gong, Biao, Yan, Chenggang
With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models. Machine unlearning has emerged as a potential solution to enable sele
Externí odkaz:
http://arxiv.org/abs/2408.14393
While generative models have made significant advancements in recent years, they also raise concerns such as privacy breaches and biases. Machine unlearning has emerged as a viable solution, aiming to remove specific training data, e.g., containing p
Externí odkaz:
http://arxiv.org/abs/2408.01689
The emergence of ChatGPT marks the arrival of the large language model (LLM) era. While LLMs demonstrate their power in a variety of fields, they also raise serious privacy concerns as the users' queries are sent to the model provider. On the other s
Externí odkaz:
http://arxiv.org/abs/2405.18744
Autor:
Liao, Xinting, Liu, Weiming, Chen, Chaochao, Zhou, Pengyang, Yu, Fengyuan, Zhu, Huabin, Yao, Binhui, Wang, Tao, Zheng, Xiaolin, Tan, Yanchao
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised learning (FUSL) with non-IID data. However, the performance of existi
Externí odkaz:
http://arxiv.org/abs/2403.16398
Autor:
Chen, Chaochao, Zhang, Yizhao, Li, Yuyuan, Wang, Jun, Qi, Lianyong, Xu, Xiaolong, Zheng, Xiaolin, Yin, Jianwei
With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as unlearning target. However, attackers can extract private in
Externí odkaz:
http://arxiv.org/abs/2403.06737