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ORCIDMiao Xu 0001
Person information
- affiliation: University of Queensland, Brisbane, QLD, Australia
- affiliation (PhD): Nanjing University, Department of Computer Science and Technology, Nanjing, China
Other persons with the same name
- Miao Xu — disambiguation page
- Miao Xu 0002
![[0000-0001-9684-0642]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-9684-0642")
— Jiangsu University, School of Automotive and Traffic Engineering, Zhenjiang, China (and 1 more)
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2020 – today
- 2025
[j10]Kun Han, Abigail M. Y. Koay, Ryan K. L. Ko, Weitong Chen, Miao Xu:
Adapting to the stream: an instance-attention GNN method for irregular multivariate time series data. Frontiers Comput. Sci. 19(8): 198340 (2025)- [j9]Yi Gao, Jing-Yi Zhu, Miao Xu, Min-Ling Zhang:
Multi-Label Learning With Multiple Complementary Labels. IEEE Trans. Pattern Anal. Mach. Intell. 47(9): 8013-8024 (2025) - [j8]Yawen Zhao, Mingzhe Zhang, Chenhao Zhang, Weitong Chen, Nan Ye, Miao Xu:
A boosting framework for positive-unlabeled learning. Stat. Comput. 35(1): 2 (2025) - [j7]Chenhao Zhang, Weitong Chen, Wei Zhang, Miao Xu:
Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting Items. ACM Trans. Intell. Syst. Technol. 16(1): 5:1-5:26 (2025) - [c40]Chenhao Zhang, Shaofei Shen, Weitong Chen, Miao Xu:
Toward Efficient Data-Free Unlearning. AAAI 2025: 22372-22379 - [c39]Xin Shen, Heming Du, Miao Xu, Miaomiao Liu, Xin Yu:
Cross-View Isolated Sign Language Recognition Challenge: Design, Results and Future Research. WWW (Companion Volume) 2025: 2444-2447 - [i29]Liangwewi Nathan Zheng, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen:
Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots and Advancing Stability. CoRR abs/2501.09283 (2025) - [i28]Wenhao Liang, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen:
PostHoc FREE Calibrating on Kolmogorov Arnold Networks. CoRR abs/2503.01195 (2025) - [i27]Wenhao Liang, Wei Zhang, Yue Lin, Miao Xu, Olaf Maennel, Weitong Chen:
We Care Each Pixel: Calibrating on Medical Segmentation Model. CoRR abs/2503.05107 (2025) - [i26]Liangwei Nathan Zheng, Wei Emma Zhang, Mingyu Guo, Miao Xu, Olaf Maennel, Weitong Chen:
Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate. CoRR abs/2505.19525 (2025) - [i25]Shaofei Shen, Chenhao Zhang, Yawen Zhao, Alina Bialkowski, Weitong Chen, Miao Xu:
Machine Unlearning for Streaming Forgetting. CoRR abs/2507.15280 (2025) - [i24]Wenhao Liang, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen:
Calibration Attention: Instance-wise Temperature Scaling for Vision Transformers. CoRR abs/2508.08547 (2025) - 2024
- [j6]Jiaqi Lv, Biao Liu, Lei Feng, Ning Xu, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama:
On the Robustness of Average Losses for Partial-Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 2569-2583 (2024) - [j5]Yi Gao, Miao Xu, Min-Ling Zhang:
Complementary to Multiple Labels: A Correlation-Aware Correction Approach. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 9179-9191 (2024) - [c38]Tong Wang, Yuan Yao, Feng Xu, Miao Xu, Shengwei An, Ting Wang:
Inspecting Prediction Confidence for Detecting Black-Box Backdoor Attacks. AAAI 2024: 274-282 - [c37]Chenhao Zhang, Weitong Chen, Wei Emma Zhang, Miao Xu:
Countering Relearning with Perception Revising Unlearning. ACML 2024: 1336-1351 - [c36]Kun Han, Abigail M. Y. Koay, Ryan K. L. Ko, Weitong Chen, Miao Xu:
Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation Approach. ADC 2024: 293-307 - [c35]Liangwei Nathan Zheng, Zhengyang Li, Chang George Dong, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen:
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics. CIKM 2024: 3405-3414 - [c34]Shaofei Shen, Chenhao Zhang, Yawen Zhao, Alina Bialkowski, Weitong Chen, Miao Xu:
Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models. ICLR 2024 - [c33]Yi Tang, Yi Gao, Yonggang Luo, Jucheng Yang, Miao Xu, Min-Ling Zhang:
Unlearning from Weakly Supervised Learning. IJCAI 2024: 5000-5008 - [c32]Miao Xu:
Machine Unlearning: Challenges in Data Quality and Access. IJCAI 2024: 8589-8594 - [c31]Mingzhe Zhang, Laura J. Ferris, Lin Yue, Miao Xu:
Emotionally Guided Symbolic Music Generation Using Diffusion Models: The AGE-DM Approach. MMAsia 2024: 127:1-127:5 - [c30]Jiaqi Lv, Yangfan Liu, Shiyu Xia, Ning Xu, Miao Xu, Gang Niu, Min-Ling Zhang, Masashi Sugiyama, Xin Geng:
What Makes Partial-Label Learning Algorithms Effective? NeurIPS 2024 - [c29]Shaofei Shen, Chenhao Zhang, Alina Bialkowski, Weitong Chen, Miao Xu:
CaMU: Disentangling Causal Effects in Deep Model Unlearning. SDM 2024: 779-787 - [i23]Shaofei Shen, Chenhao Zhang, Alina Bialkowski, Weitong Chen, Miao Xu:
CaMU: Disentangling Causal Effects in Deep Model Unlearning. CoRR abs/2401.17504 (2024) - [i22]Shaofei Shen, Chenhao Zhang, Yawen Zhao, Alina Bialkowski, Weitong Chen, Miao Xu:
Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models. CoRR abs/2404.00506 (2024) - [i21]Chenhao Zhang, Shaofei Shen, Yawen Zhao, Weitong Tony Chen, Miao Xu:
GENIU: A Restricted Data Access Unlearning for Imbalanced Data. CoRR abs/2406.07885 (2024) - [i20]Liangwei Nathan Zheng, Zhengyang Li, Chang George Dong, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen:
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics. CoRR abs/2410.12257 (2024) - [i19]Liangwei Nathan Zheng, Chang George Dong, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen:
Revisited Large Language Model for Time Series Analysis through Modality Alignment. CoRR abs/2410.12326 (2024) - [i18]Chenhao Zhang, Shaofei Shen, Weitong Chen, Miao Xu:
Toward Efficient Data-Free Unlearning. CoRR abs/2412.13790 (2024) - 2023
- [j4]Yixuan Qiu, Feng Lin, Weitong Chen, Miao Xu:
Pre-training in Medical Data: A Survey. Mach. Intell. Res. 20(2): 147-179 (2023) - [c28]Yixuan Qiu, Weitong Chen, Miao Xu:
A Progressive Sampling Method for Dual-Node Imbalanced Learning with Restricted Data Access. ICDM 2023: 508-517 - [c27]Yi Gao, Miao Xu, Min-Ling Zhang:
Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning. IJCAI 2023: 3732-3740 - [c26]Shaofei Shen, Mingzhe Zhang, Weitong Chen, Alina Bialkowski, Miao Xu:
Words Can Be Confusing: Stereotype Bias Removal in Text Classification at the Word Level. PAKDD (4) 2023: 99-111 - [i17]Yi Gao, Miao Xu, Min-Ling Zhang:
Complementary to Multiple Labels: A Correlation-Aware Correction Approach. CoRR abs/2302.12987 (2023) - 2022
- [j3]Guanhua Ye, Hongzhi Yin, Tong Chen, Miao Xu, Quoc Viet Hung Nguyen, Jiangning Song:
Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent. IEEE J. Biomed. Health Informatics 26(6): 2778-2786 (2022) - [c25]Mingzhe Zhang, Lin Yue, Miao Xu:
ESTD: Empathy Style Transformer with Discriminative Mechanism. ADMA (2) 2022: 58-72 - [c24]Yawen Zhao, Lin Yue, Miao Xu:
A Boosting Algorithm for Training from Only Unlabeled Data. ADMA (2) 2022: 459-473 - [c23]Shaofei Shen, Miao Xu, Lin Yue, Robert Boots, Weitong Chen:
Death Comes But Why: An Interpretable Illness Severity Predictions in ICU. APWeb/WAIM (1) 2022: 60-75 - [c22]Khai Phan Tran, Weitong Chen, Miao Xu:
Improving Traffic Load Prediction with Multi-modality - A Case Study of Brisbane. AI 2022: 254-266 - [c21]Shaofei Shen, Weitong Chen, Miao Xu:
What Leads to Arrhythmia: Active Causal Representation Learning of ECG Classification. AI 2022: 501-515 - [c20]Kun Han, Weitong Chen, Miao Xu:
Investigating Active Positive-Unlabeled Learning with Deep Networks. AI 2022: 607-618 - [c19]Ji Liu, Zenan Li, Yuan Yao, Feng Xu, Xiaoxing Ma, Miao Xu, Hanghang Tong:
Fair Representation Learning: An Alternative to Mutual Information. KDD 2022: 1088-1097 - [c18]Jonathan Wilton, Abigail M. Y. Koay, Ryan K. L. Ko, Miao Xu, Nan Ye:
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization. NeurIPS 2022 - [c17]Chenhao Zhang, Yanjun Zhang, Jeff Mao, Weitong Chen, Lin Yue, Guangdong Bai, Miao Xu:
Towards Better Generalization for Neural Network-Based SAT Solvers. PAKDD (2) 2022: 199-210 - [i16]Yawen Zhao, Mingzhe Zhang, Chenhao Zhang, Tony Chen, Nan Ye, Miao Xu:
A Boosting Algorithm for Positive-Unlabeled Learning. CoRR abs/2205.09485 (2022) - [i15]Tong Wang, Yuan Yao, Feng Xu, Miao Xu, Shengwei An, Ting Wang:
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer. CoRR abs/2208.06592 (2022) - [i14]Jonathan Wilton, Abigail M. Y. Koay, Ryan K. L. Ko, Miao Xu, Nan Ye:
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization. CoRR abs/2210.08461 (2022) - 2021
- [j2]Miao Xu, Lan-Zhe Guo:
Learning from group supervision: the impact of supervision deficiency on multi-label learning. Sci. China Inf. Sci. 64(3) (2021) - [c16]Yixuan Qiu, Weitong Chen, Lin Yue, Miao Xu, Baofeng Zhu:
STCT: Spatial-Temporal Conv-Transformer Network for Cardiac Arrhythmias Recognition. ADMA 2021: 86-100 - [c15]Yanda Wang, Weitong Chen, Dechang Pi, Lin Yue, Miao Xu, Xue Li:
Multi-hop Reading on Memory Neural Network with Selective Coverage for Medication Recommendation. CIKM 2021: 2020-2029 - [c14]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. ICML 2021: 3252-3262 - [c13]Guangxin Su, Weitong Chen, Miao Xu:
Positive-Unlabeled Learning from Imbalanced Data. IJCAI 2021: 2995-3001 - [c12]Yanda Wang, Weitong Chen, Dechang Pi, Lin Yue, Sen Wang, Miao Xu:
Self-Supervised Adversarial Distribution Regularization for Medication Recommendation. IJCAI 2021: 3134-3140 - [i13]Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama:
On the Robustness of Average Losses for Partial-Label Learning. CoRR abs/2106.06152 (2021) - [i12]Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. CoRR abs/2109.14676 (2021) - [i11]Guanhua Ye, Hongzhi Yin, Tong Chen, Miao Xu, Quoc Viet Hung Nguyen, Jiangning Song:
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent. CoRR abs/2112.09341 (2021) - 2020
- [j1]Miao Xu, Yufeng Li, Zhi-Hua Zhou:
Robust Multi-Label Learning with PRO Loss. IEEE Trans. Knowl. Data Eng. 32(8): 1610-1624 (2020) - [c11]Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. ICML 2020: 4006-4016 - [c10]Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama:
Progressive Identification of True Labels for Partial-Label Learning. ICML 2020: 6500-6510 - [c9]Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong:
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering. NeurIPS 2020 - [c8]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. NeurIPS 2020 - [i10]Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama:
Progressive Identification of True Labels for Partial-Label Learning. CoRR abs/2002.08053 (2020) - [i9]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. CoRR abs/2007.08929 (2020) - [i8]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. CoRR abs/2010.01875 (2020)
2010 – 2019
- 2019
- [c7]Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama:
Clipped Matrix Completion: A Remedy for Ceiling Effects. AAAI 2019: 5151-5158 - [i7]Miao Xu, Bingcong Li, Gang Niu, Bo Han, Masashi Sugiyama:
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. CoRR abs/1901.10155 (2019) - 2018
- [c6]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. KDD 2018: 1571-1579 - [c5]Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-teaching: Robust training of deep neural networks with extremely noisy labels. NeurIPS 2018: 8536-8546 - [i6]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. CoRR abs/1802.05380 (2018) - [i5]Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-sampling: Training Robust Networks for Extremely Noisy Supervision. CoRR abs/1804.06872 (2018) - [i4]Miao Xu, Gang Niu, Bo Han, Ivor W. Tsang, Zhi-Hua Zhou, Masashi Sugiyama:
Matrix Co-completion for Multi-label Classification with Missing Features and Labels. CoRR abs/1805.09156 (2018) - [i3]Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama:
Clipped Matrix Completion: a Remedy for Ceiling Effects. CoRR abs/1809.04997 (2018) - [i2]Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. CoRR abs/1809.11008 (2018) - 2017
- [c4]Miao Xu, Zhi-Hua Zhou:
Incomplete Label Distribution Learning. IJCAI 2017: 3175-3181 - 2015
- [c3]Miao Xu, Rong Jin, Zhi-Hua Zhou:
CUR Algorithm for Partially Observed Matrices. ICML 2015: 1412-1421 - 2014
- [i1]Miao Xu, Rong Jin, Zhi-Hua Zhou:
CUR Algorithm for Partially Observed Matrices. CoRR abs/1411.0860 (2014) - 2013
- [c2]Miao Xu, Yufeng Li, Zhi-Hua Zhou:
Multi-Label Learning with PRO Loss. AAAI 2013: 998-1004 - [c1]Miao Xu, Rong Jin, Zhi-Hua Zhou:
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning. NIPS 2013: 2301-2309
Coauthor Index
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