Miao Xu

Orcid: 0000-0001-9409-6960

Affiliations:
  • University of Queensland, Brisbane, QLD, Australia
  • Nanjing University, Department of Computer Science and Technology, Nanjing, China (PhD)


According to our database1, Miao Xu authored at least 66 papers between 2013 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2025
Multi-Label Learning With Multiple Complementary Labels.
IEEE Trans. Pattern Anal. Mach. Intell., September, 2025

Adapting to the stream: an instance-attention GNN method for irregular multivariate time series data.
Frontiers Comput. Sci., August, 2025

Calibration Attention: Instance-wise Temperature Scaling for Vision Transformers.
CoRR, August, 2025

Machine Unlearning for Streaming Forgetting.
CoRR, July, 2025

Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate.
CoRR, May, 2025

We Care Each Pixel: Calibrating on Medical Segmentation Model.
CoRR, March, 2025

PostHoc FREE Calibrating on Kolmogorov Arnold Networks.
CoRR, March, 2025

Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting Items.
ACM Trans. Intell. Syst. Technol., February, 2025

A boosting framework for positive-unlabeled learning.
Stat. Comput., February, 2025

Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots and Advancing Stability.
CoRR, January, 2025

Cross-View Isolated Sign Language Recognition Challenge: Design, Results and Future Research.
Proceedings of the Companion Proceedings of the ACM on Web Conference 2025, 2025

Toward Efficient Data-Free Unlearning.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
Complementary to Multiple Labels: A Correlation-Aware Correction Approach.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

On the Robustness of Average Losses for Partial-Label Learning.
IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

Revisited Large Language Model for Time Series Analysis through Modality Alignment.
CoRR, 2024

GENIU: A Restricted Data Access Unlearning for Imbalanced Data.
CoRR, 2024

CaMU: Disentangling Causal Effects in Deep Model Unlearning.
Proceedings of the 2024 SIAM International Conference on Data Mining, 2024

What Makes Partial-Label Learning Algorithms Effective?
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Emotionally Guided Symbolic Music Generation Using Diffusion Models: The AGE-DM Approach.
Proceedings of the 6th ACM International Conference on Multimedia in Asia, 2024

Machine Unlearning: Challenges in Data Quality and Access.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Unlearning from Weakly Supervised Learning.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Mining Irregular Time Series Data with Noisy Labels: A Risk Estimation Approach.
Proceedings of the Databases Theory and Applications, 2024

Countering Relearning with Perception Revising Unlearning.
Proceedings of the Asian Conference on Machine Learning, 2024

Inspecting Prediction Confidence for Detecting Black-Box Backdoor Attacks.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Pre-training in Medical Data: A Survey.
Mach. Intell. Res., April, 2023

Words Can Be Confusing: Stereotype Bias Removal in Text Classification at the Word Level.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

A Progressive Sampling Method for Dual-Node Imbalanced Learning with Restricted Data Access.
Proceedings of the IEEE International Conference on Data Mining, 2023

2022
Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent.
IEEE J. Biomed. Health Informatics, 2022

Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer.
CoRR, 2022

A Boosting Algorithm for Positive-Unlabeled Learning.
CoRR, 2022

Towards Better Generalization for Neural Network-Based SAT Solvers.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fair Representation Learning: An Alternative to Mutual Information.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Improving Traffic Load Prediction with Multi-modality - A Case Study of Brisbane.
Proceedings of the AI 2021: Advances in Artificial Intelligence, 2022

What Leads to Arrhythmia: Active Causal Representation Learning of ECG Classification.
Proceedings of the AI 2022: Advances in Artificial Intelligence, 2022

Investigating Active Positive-Unlabeled Learning with Deep Networks.
Proceedings of the AI 2021: Advances in Artificial Intelligence, 2022

Death Comes But Why: An Interpretable Illness Severity Predictions in ICU.
Proceedings of the Web and Big Data - 6th International Joint Conference, 2022

A Boosting Algorithm for Training from Only Unlabeled Data.
Proceedings of the Advanced Data Mining and Applications - 18th International Conference, 2022

ESTD: Empathy Style Transformer with Discriminative Mechanism.
Proceedings of the Advanced Data Mining and Applications - 18th International Conference, 2022

2021
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach.
CoRR, 2021

On the Robustness of Average Losses for Partial-Label Learning.
CoRR, 2021

Learning from group supervision: the impact of supervision deficiency on multi-label learning.
Sci. China Inf. Sci., 2021

Self-Supervised Adversarial Distribution Regularization for Medication Recommendation.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Positive-Unlabeled Learning from Imbalanced Data.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Pointwise Binary Classification with Pairwise Confidence Comparisons.
Proceedings of the 38th International Conference on Machine Learning, 2021

Multi-hop Reading on Memory Neural Network with Selective Coverage for Medication Recommendation.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

STCT: Spatial-Temporal Conv-Transformer Network for Cardiac Arrhythmias Recognition.
Proceedings of the Advanced Data Mining and Applications - 17th International Conference, 2021

2020
Robust Multi-Label Learning with PRO Loss.
IEEE Trans. Knowl. Data Eng., 2020

Provably Consistent Partial-Label Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Progressive Identification of True Labels for Partial-Label Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative.
CoRR, 2019

Clipped Matrix Completion: A Remedy for Ceiling Effects.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels.
CoRR, 2018

Matrix Co-completion for Multi-label Classification with Missing Features and Labels.
CoRR, 2018

Co-sampling: Training Robust Networks for Extremely Noisy Supervision.
CoRR, 2018

Co-teaching: Robust training of deep neural networks with extremely noisy labels.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Active Feature Acquisition with Supervised Matrix Completion.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017
Incomplete Label Distribution Learning.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2015
CUR Algorithm for Partially Observed Matrices.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2013
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Multi-Label Learning with PRO Loss.
Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013


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