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Active Refinement for Multi-Label Learning: A Pseudo-Label Approach


Sep 29, 2021
Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

* A preliminary version appeared in the Workshop on Learning from Limited Labeled Data @ ICLR 2019 

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A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising


Jun 11, 2021
Chao Wen, Miao Xu, Zhilin Zhang, Zhenzhe Zheng, Yuhui Wang, Xiangyu Liu, Yu Rong, Dong Xie, Xiaoyang Tan, Chuan Yu, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu

* 10 pages 

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On the Robustness of Average Losses for Partial-Label Learning


Jun 11, 2021
Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama


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Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising


Jan 08, 2021
Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, Kun Gai

* To appear in the Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM), 2021 

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Pointwise Binary Classification with Pairwise Confidence Comparisons


Oct 05, 2020
Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama


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Learning to Infer User Hidden States for Online Sequential Advertising


Sep 03, 2020
Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Weinan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

* to be published in CIKM 2020 

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Provably Consistent Partial-Label Learning


Jul 17, 2020
Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama


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Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising


Jun 29, 2020
Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, Han Li, Jian Xu, Kun Gai

* accepted by ICML 2020 

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Progressive Identification of True Labels for Partial-Label Learning


Feb 19, 2020
Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama


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Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative


Jan 29, 2019
Miao Xu, Bingcong Li, Gang Niu, Bo Han, Masashi Sugiyama


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Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels


Oct 30, 2018
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama

* NIPS 2018 camera-ready version 

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Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels


Sep 28, 2018
Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor Tsang, Masashi Sugiyama


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Clipped Matrix Completion: a Remedy for Ceiling Effects


Sep 13, 2018
Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama

* 35 pages, 3 figures 

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Active Feature Acquisition with Supervised Matrix Completion


Jun 05, 2018
Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen

* 9 pages, 8 figures 

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Matrix Co-completion for Multi-label Classification with Missing Features and Labels


May 23, 2018
Miao Xu, Gang Niu, Bo Han, Ivor W. Tsang, Zhi-Hua Zhou, Masashi Sugiyama


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CUR Algorithm for Partially Observed Matrices


Nov 04, 2014
Miao Xu, Rong Jin, Zhi-Hua Zhou


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