Xu, Miao

25 publications

NeurIPS 2025 ComRank: Ranking Loss for Multi-Label Complementary Label Learning Jing-Yi Zhu, Yi Gao, Miao Xu, Min-Ling Zhang

AAAI 2025 Toward Efficient Data-Free Unlearning Chenhao Zhang, Shaofei Shen, Weitong Chen, Miao Xu

ACML 2024 Countering Relearning with Perception Revising Unlearning Chenhao Zhang, Weitong Chen, Wei Emma Zhang, Miao Xu

AAAI 2024 Inspecting Prediction Confidence for Detecting Black-Box Backdoor Attacks Tong Wang, Yuan Yao, Feng Xu, Miao Xu, Shengwei An, Ting Wang

ICLR 2024 Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models Shaofei Shen, Chenhao Zhang, Yawen Zhao, Alina Bialkowski, Weitong Tony Chen, Miao Xu

IJCAI 2024 Machine Unlearning: Challenges in Data Quality and Access Miao Xu

IJCAI 2024 Unlearning from Weakly Supervised Learning Yi Tang, Yi Gao, Yonggang Luo, Jucheng Yang, Miao Xu, Min-Ling Zhang

NeurIPS 2024 What Makes Partial-Label Learning Algorithms Effective? Jiaqi Lv, Yangfan Liu, Shiyu Xia, Ning Xu, Miao Xu, Gang Niu, Min-Ling Zhang, Masashi Sugiyama, Xin Geng

IJCAI 2023 Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning Yi Gao, Miao Xu, Min-Ling Zhang

NeurIPS 2022 Positive-Unlabeled Learning Using Random Forests via Recursive Greedy Risk Minimization Jonathan Wilton, Abigail Koay, Ryan Ko, Miao Xu, Nan Ye

ICML 2021 Pointwise Binary Classification with Pairwise Confidence Comparisons Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama

IJCAI 2021 Positive-Unlabeled Learning from Imbalanced Data Guangxin Su, Weitong Chen, Miao Xu

IJCAI 2021 Self-Supervised Adversarial Distribution Regularization for Medication Recommendation Yanda Wang, Weitong Chen, Dechang Pi, Lin Yue, Sen Wang, Miao Xu

ICML 2020 Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising 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

ICML 2020 Progressive Identification of True Labels for Partial-Label Learning Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama

NeurIPS 2020 Provably Consistent Partial-Label Learning Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama

ICML 2020 SIGUA: Forgetting May Make Learning with Noisy Labels More Robust Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama

NeurIPS 2020 Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong

ICLRW 2019 A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision Cheng-Yu Hsieh, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

AAAI 2019 Clipped Matrix Completion: A Remedy for Ceiling Effects Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama

NeurIPS 2018 Co-Teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama

IJCAI 2017 Incomplete Label Distribution Learning Miao Xu, Zhi-Hua Zhou

ICML 2015 CUR Algorithm for Partially Observed Matrices Miao Xu, Rong Jin, Zhi-Hua Zhou

AAAI 2013 Multi-Label Learning with PRO Loss Miao Xu, Yufeng Li, Zhi-Hua Zhou

NeurIPS 2013 Speedup Matrix Completion with Side Information: Application to Multi-Label Learning Miao Xu, Rong Jin, Zhi-Hua Zhou