Zobrazeno 1 - 10
of 1 094
pro vyhledávání: '"Liu, Weiming"'
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:
Wang, Yingrong, Wu, Anpeng, Li, Haoxuan, Liu, Weiming, Miao, Qiaowei, Xiong, Ruoxuan, Wu, Fei, Kuang, Kun
This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other. For example, a h
Externí odkaz:
http://arxiv.org/abs/2403.02624
Publikováno v:
Proceedings of the 31st ACM International Conference on Multimedia. 2023: 6321-6331
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interaction data encounter performance degradation when the sequences are short
Externí odkaz:
http://arxiv.org/abs/2402.14473
Short text clustering has been popularly studied for its significance in mining valuable insights from many short texts. In this paper, we focus on the federated short text clustering (FSTC) problem, i.e., clustering short texts that are distributed
Externí odkaz:
http://arxiv.org/abs/2312.07556
Graph clustering has been popularly studied in recent years. However, most existing graph clustering methods focus on node-level clustering, i.e., grouping nodes in a single graph into clusters. In contrast, graph-level clustering, i.e., grouping mul
Externí odkaz:
http://arxiv.org/abs/2311.13953
Autor:
Wang, Hao, Chen, Zhichao, Fan, Jiajun, Li, Haoxuan, Liu, Tianqiao, Liu, Weiming, Dai, Quanyu, Wang, Yichao, Dong, Zhenhua, Tang, Ruiming
Estimating conditional average treatment effect from observational data is highly challenging due to the existence of treatment selection bias. Prevalent methods mitigate this issue by aligning distributions of different treatment groups in the laten
Externí odkaz:
http://arxiv.org/abs/2310.18286
Autor:
Han, Zhongxuan, Chen, Chaochao, Zheng, Xiaolin, Liu, Weiming, Wang, Jun, Cheng, Wenjie, Li, Yuyuan
Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The existing research on UOF is limited and fails to deal with the root caus
Externí odkaz:
http://arxiv.org/abs/2309.01335
Autor:
Liao, Xinting, Chen, Chaochao, Liu, Weiming, Zhou, Pengyang, Zhu, Huabin, Shen, Shuheng, Wang, Weiqiang, Hu, Mengling, Tan, Yanchao, Zheng, Xiaolin
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective in real-world applications, existing work devotes to improving the mode
Externí odkaz:
http://arxiv.org/abs/2308.11646
Autor:
Liao, Xinting, Liu, Weiming, Chen, Chaochao, Zhou, Pengyang, Zhu, Huabin, Tan, Yanchao, Wang, Jun, Qi, Yue
Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the clas
Externí odkaz:
http://arxiv.org/abs/2307.14384
Policy-Space Response Oracles (PSRO) is an influential algorithm framework for approximating a Nash Equilibrium (NE) in multi-agent non-transitive games. Many previous studies have been trying to promote policy diversity in PSRO. A major weakness in
Externí odkaz:
http://arxiv.org/abs/2306.16884