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217 results on '"Tan, Juntao"'

57. OHCCPredictor: an online risk stratification model for predicting survival duration of older patients with hepatocellular carcinoma

Author

Tan, Juntao, primary, Yu, Yue, additional, Lin, Xiantian, additional, He, Yuxin, additional, Jin, Wen, additional, Qian, Hong, additional, Li, Ying, additional, Xu, Xiaomei, additional, Zhao, Yuxi, additional, Ning, Jianwen, additional, Zhang, Zhengyu, additional, Chen, Jingjing, additional, and Wu, Xiaoxin, additional

Published
2023
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59. Counterfactual Collaborative Reasoning

Author

Ji, Jianchao, primary, Li, Zelong, additional, Xu, Shuyuan, additional, Xiong, Max, additional, Tan, Juntao, additional, Ge, Yingqiang, additional, Wang, Hao, additional, and Zhang, Yongfeng, additional

Published
2023
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64. Efficient Non-Sampling Graph Neural Networks.

Author

Ji, Jianchao, Li, Zelong, Xu, Shuyuan, Ge, Yingqiang, Tan, Juntao, and Zhang, Yongfeng

Subjects
ARTIFICIAL neural networks, DATA structures, TIME complexity, FRACTIONS, SAMPLING (Process)
Abstract

A graph is a widely used and effective data structure in many applications; it describes the relationships among nodes or entities. Currently, most semi-supervised or unsupervised graph neural network models are trained based on a very basic operation called negative sampling. Usually, the purpose of the learning objective is to maximize the similarity between neighboring nodes while minimizing the similarity between nodes that are not close to each other. Negative sampling can reduce the time complexity by sampling a small fraction of the negative nodes instead of using all of the negative nodes when optimizing the objective. However, sampling of the negative nodes may fail to deliver stable model performance due to the uncertainty in the sampling procedure. To avoid such disadvantages, we provide an efficient Non-Sampling Graph Neural Network (NS-GNN) framework. The main idea is to use all the negative samples when optimizing the learning objective to avoid the sampling process. Of course, directly using all of the negative samples may cause a large increase in the model training time. To mitigate this problem, we rearrange the origin loss function into a linear form and take advantage of meticulous mathematical derivation to reduce the complexity of the loss function. Experiments on benchmark datasets show that our framework can provide better efficiency at the same level of prediction accuracy compared with existing negative sampling-based models. [ABSTRACT FROM AUTHOR]

Published
2023
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75. Explainable Fairness in Recommendation

Author

Ge, Yingqiang, primary, Tan, Juntao, additional, Zhu, Yan, additional, Xia, Yinglong, additional, Luo, Jiebo, additional, Liu, Shuchang, additional, Fu, Zuohui, additional, Geng, Shijie, additional, Li, Zelong, additional, and Zhang, Yongfeng, additional

Published
2022
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76. Fairness in Recommendation: A Survey

Author

Li, Yunqi, Chen, Hanxiong, Xu, Shuyuan, Ge, Yingqiang, Tan, Juntao, Liu, Shuchang, and Zhang, Yongfeng

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Information Retrieval (cs.IR), Machine Learning (cs.LG), Computer Science - Information Retrieval
Abstract

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond., 38 pages, 2 figures, 1 table

Published
2022

91. Efficient Federated-Learning Model Debugging

Author

Li, Anran, primary, Zhang, Lan, additional, Wang, Junhao, additional, Tan, Juntao, additional, Han, Feng, additional, Qin, Yaxuan, additional, Freris, Nikolaos M., additional, and Li, Xiang-Yang, additional

Published
2021
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100. Regional economic resilience of resource‐based cities and influential factors during economic crises in China.

Author

Tan, Juntao, Lo, Kevin, Qiu, Fangdao, Zhang, Xinlin, and Zhao, Hongbo

Subjects
*FINANCIAL crises, *BUSINESS cycles, *ECONOMIC impact, *NATURAL resources, *PRICE fluctuations
Abstract

Resource‐based cities (RBCs) whose economies depend primarily on exploiting and processing natural resources usually have rigid, singular, and low‐end industrial structures, which often cripples their ability to cope with external disturbances such as international resource price fluctuations and economic downturns. This paper quantitatively analyzes the economic resilience of RBCs in China in terms of resistance and recoverability during the Asian financial crisis and the global financial crisis. Furthermore, it identifies the main factors affecting resilience. There are four main findings: First, RBCs were quickly and negatively impacted by the Asian financial crisis, which suggests that economic resistance was generally low during this period. In the recovery period, while the rate of recovery was slow at the beginning, economic recoverability improved after 2002. Economic resistance and recoverability were found to have a strong negative correlation. Second, at the beginning of the global financial crisis, the economic resistance of RBCs was generally high. However, after 2012, the number of cities that were severely affected by the economic crisis increased rapidly. Third, economic resistance varied across different types of RBCs. Coal‐based and forestry‐based cities had lower economic resistance, while oil & gas‐based cities were more resistant. RBCs in the Eastern region generally had low economic resistance, while the economic resilience of recessionary cities was also low. Finally, while factors affecting the economic resilience varied across the two economic cycles, we found that economic development, labor conditions and, most of all, the industrial structure had a statistically significant negative effect on economic resilience. [ABSTRACT FROM AUTHOR]

Published
2020
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