People_

Associate Professor Tongliang Liu

BEng, PhD

ARC Future Fellow
Director of Sydney Artificial Intelligence Centre
Director of Trustworthy Machine Learning Lab
School of Computer Science

Phone

+61 2 8627 5966

Email

tongliang.liu@sydney.edu.au

Address

J12 - Computer Science Building

The University of Sydney

Websites

Sydney Artificial Intelligence Centre

Trustworthy Machine Learning Lab

Personal page

Biographical details

Tongliang Liu is an Associate Professor in Machine Learning with the School of Computer Science and The Director of Sydney AI Centre at the University of Sydney. He is broadly interested in the fields of trustworthy machine learning and its interdisciplinary applications, with a particular emphasis on learning with noisy labels, adversarial learning, causal representation learning, transfer learning, unsupervised learning, foundation model safety, and statistical deep learning theory. He has authored and co-authored more than 200 research articles including ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, AAAI, IJCAI, JMLR, and TPAMI. He is/was a senior meta-reviewer for NeurIPS, ICLR, AAAI, and IJCAI, meta-reviewer for NeurIPS, ICML, ICLR, UAI, AAAI, IJCAI, and KDD, and was a notable AC for NeurIPS and ICLR. He is a co-Editor-in-Chief for Neural Networks, an Associate Editor of IEEE TPAMI, TIP, ACM Computing Surveys, and TMLR, and is on the Editorial Boards of JMLR and MLJ. He is a recipient of CORE Award for Outstanding Research Contribution in 2024, the IEEE AI’s 10 to Watch Award in 2023, the Future Fellowship Award from Australian Research Council (ARC) in 2022, the Top-40 Early Achievers by The Australian in 2020, and the Discovery Early Career Researcher Award (DECRA) from ARC in 2018.

Research interests

In our daily lives, we make many decisions. Some are simple, but some are not. Why not let machines help? This is the principle underlying Dr Tongliang Liu's research into machine learning. He designs effective, efficient, and understandable learning algorithms - the processes machines use to "think" through datasets in order to "decide" how to respond to a problem.

"Machine learning is similar to human learning. As humans, when we encounter a problem, we think through our store of learned experience and knowledge to identify a rule that we think will apply to this situation, and we base our decision on that.

"Similarly, in machine learning, a machine presented with a problem will run algorithms ('think') through data ('experience and knowledge') to identify an applicable hypothesis ('rule'), and base its response ('decision') on that.

"My research aims to provide mathematical justifications for existing learning algorithms, and to design more effective and efficient learning algorithms for real-world problems in data mining and computer vision.

"For example, as we humans get older and gain more experience and knowledge, the decisions we make become more reliable. A similar phenomenon applies to machine learning as the amount of data collected increases. My research focuses on understanding this process, and how we can exploit it to make machine-made decisions more reliable.

"The ultimate goal would be to make machines as clever as humans. If we can develop machines that have even some of the decision-making capabilities of humans, this could reduce human labour and inconvenience and improve our quality of life. This is what motivates me to do research in machine learning.

"What excites me about this field is that recently some machine-learning algorithms have outperformed humans in several difficult problems, such as face recognition and playing the board game Go.

"I have been working in this field since 2012, and joined the University of Sydney in early 2017. The outstanding research staff and students here inspire me enormously. I believe that together we can achieve great things."

Teaching and supervision

COMP5318- Machine Learning and Data Mining

COMP5328- Advanced Machine Learning

Awards and honours

Award for Outstanding Research Contribution - CORE 2024
IEEE AI's 10 to Watch Award - IEEE 2023
Eureka Prize for Emerging Leaders shortlist - Australian Museum 2023
Notable Area Chair - ICLR 2023
Future Fellowship Award - ARC 2022
OPPO Faculty Award - OPPO 2022
Meituan Faculty Research Award for Collaboration Exploration 2022
Best Paper Honorable Mention - IEEE MMSP 2022
Best Paper Award - ACM Mobiarch 2022
Early Career Research Excellence – Faculty of Engineering, University of Sydney 2021
Best VisNotes Paper Award - PacificVis 2021
Bioengineering and Digital Science Catalyst Award - Cardiovascular Initiative 2021
Expert Reviewer - ICML 2021
Early Career Researcher Award Honourable Mention - Australian Pattern Recognition Society (APRS) 2020
Top 10% of high-scoring reviewers - NeurIPS 2020
Named in the Early Achievers Leadboard - The Australian 2020
Best Paper Award - IEEE International Conference on Multimedia & Expo (ICME) 2019
J G Russell Award Shortlisted - Australian Academy of Science (AAS) 2019
Discovery Early Career Research Award - Australian Research Council (ARC) 2019
One of the top 10 reviewers in 2018 – IET Computer Vision 2018
Distinguished Paper Candidate - International Joint Conference on Artificial Intelligence (IJCAI) 2017
IEEE Transactions on Cybernetics Outstanding Reviewer - IEEE 2016
Best Paper Award - IEEE International Conference on Information Science & Tech (ICIST) 2014
Computational Statistics & Data Analysis Outstanding Reviewer - ELSEVIER 2014

Current research students

Project title	Research student
Estimating heterogeneous treatment effect in cross-sectional and panel settings	Jack Ting-tse CHEN
Aligning with Human Objectives via Reinforcement Learning	Li HE
Generalisable Machine Learning	Zhuo HUANG
Research of Transformer about Hidden Principle and Efficiency Improvement	Leo LI
Towards Trustworthy Pattern Mining from Unreliable Environments	Muyang LI
Efficient Robustness Evaluation via Jailbreaking Attack and Robustness Enhancement by Mitigating Catastrophic Overfitting	Runqi LIN
Causality-inspired transfer learning	Yexiong LIN
Answerability, Calibration and Grounding Guarantees for Natural Language Question Answering	Kent O'SULLIVAN
Machine Learning Accelerated Molecular Dynamics	Jun WANG
Towards Explainable Online Continual Learning	Hang YU
3D Editing in Gaussian Splatting	Longjie ZHAO
Reliable deep model validation using cheap data	Jiyang ZHENG
3D Referring Comprehension and Segmentation	Mike ZHOU

Publications

By Type

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Journals

Wu, S., Zhou, T., Du, Y., Yu, J., Han, B., Liu, T. (2024). A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Zhang, J., Song, B., Wang, H., Han, B., Liu, T., Liu, L., Sugiyama, M. (2024). BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(6), 4398-4409. [More Information]
Li, M., Zhou, T., Han, B., Liu, T., Liang, X., Zhao, J., Gong, C. (2024). Class-wise Contrastive Prototype Learning for Semi-Supervised Classification under Intersectional Class Mismatch. IEEE Transactions on Multimedia. [More Information]
Yang, E., Wang, Z., Shen, L., Yin, N., Liu, T., Guo, G., Wang, X., Tao, D. (2024). Continual Learning From a Stream of APIs. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Fu, S., Ma, X., Zhan, Y., You, F., Peng, Q., Liu, T., Bailey, J., Mandic, D. (2024). Finding core labels for maximizing generalization of graph neural networks. Neural Networks, 180, 106635. [More Information]
Yang, Y., Lin, C., Li, Q., Zhao, Z., Fan, H., Zhou, D., Wang, N., Liu, T., Shen, C. (2024). Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization. IEEE Transactions on Information Forensics and Security. [More Information]
Wang, J., Xia, X., Lan, L., Wu, X., Yu, J., Yang, W., Han, B., Liu, T. (2024). Tackling Noisy Labels With Network Parameter Additive Decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Liu, L., Wang, N., Liu, D., Yang, X., Gao, X., Liu, T. (2024). Towards Specific Domain Prompt Learning Via Improved Text Label Optimization. IEEE Transactions on Multimedia. [More Information]
Yang, S., Wu, S., Yang, E., Han, B., Liu, Y., Xu, M., Niu, G., Liu, T. (2023). A Parametrical Model for Instance-Dependent Label Noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 14055-14068. [More Information]
Li, X., Xia, X., Zhu, F., Liu, T., Zhang, X., Liu, C. (2023). Dynamics-aware loss for learning with label noise. Pattern Recognition, 144, 109835. [More Information]
Xia, X., Han, B., Wang, N., Deng, J., Li, J., Mao, Y., Liu, T. (2023). Extended T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3047-3058. [More Information]
Zhang, J., Liu, T., Tao, D. (2023). Going Deeper, Generalizing Better: An Information-Theoretic View for Deep Learning. IEEE Transactions on Neural Networks and Learning Systems. [More Information]
Guo, X., Liu, J., Liu, T., Yuan, Y. (2023). Handling Open-set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, , 1-16. [More Information]
Wang, T., Shen, L., Fan, Q., Xu, T., Liu, T., Xiong, H. (2023). Joint Admission Control and Resource Allocation of Virtual Network Embedding Via Hierarchical Deep Reinforcement Learning. IEEE Transactions on Services Computing, , 1-14. [More Information]
Liu, C., Zhan, Y., Yu, B., Liu, L., Du, B., Hu, W., Liu, T. (2023). On exploring node-feature and graph-structure diversities for node drop graph pooling. Neural Networks, 167, 559-571. [More Information]
Chan, A., Wu, S., Vernon, S., Tang, O., Figtree, G., Liu, T., Yang, J., Patrick, E. (2023). Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk. iScience, 26(5), Article 106633 - 1-Article 106633 - 17. [More Information]
Shen, J., Yao, Y., Huang, S., Wang, Z., Zhang, J., Wang, R., Yu, J., Liu, T. (2023). ProtoSimi: label correction for fine-grained visual categorization. Machine Learning. [More Information]
Tian, J., Wu, X., Hsieh, M., Liu, T., Yang, W., Tao, D., Sun, X., Du, Y., Zhao, S., Liu, Q., Zhang, K., et al (2023). Recent Advances for Quantum Neural Networks in Generative Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12321-12340. [More Information]
Xia, X., Lu, P., Gong, C., Han, B., Yu, J., Yu, J., Liu, T. (2023). Regularly Truncated M-Estimators for Learning With Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Ma, J., Liu, J., Wang, Y., Li, J., Liu, T. (2023). Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering. IEEE Transactions on Neural Networks and Learning Systems, 34(1), 15-27. [More Information]
Li, S., Liu, T., Tan, J., Zeng, D., Ge, S. (2023). Trustable Co-Label Learning From Multiple Noisy Annotators. IEEE Transactions on Multimedia, 25, 1045-1057. [More Information]
Cai, S., Hong, S., Shen, J., Liu, T. (2022). A Machine Learning Approach for Predicting Human Preference for Graph Drawings. Journal of Graph Algorithms and Applications, 26(4), 447-470. [More Information]
Cai, S., Hong, S., Xia, X., Liu, T., Huang, W. (2022). A machine learning approach for predicting human shortest path task performance. Visual Informatics, 6(2), 50-61. [More Information]
Yang, S., Wu, S., Liu, T., Xu, M. (2022). Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 9830-9843. [More Information]
Bao, G., Chen, H., Liu, T., Gong, G., Yin, Y., Wang, L., Wang, X. (2022). COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. Pattern Recognition, 124, 108499. [More Information]
Yin, X., Du, Y., Fei, Y., Zhang, R., Liu, L., Mao, Y., Liu, T., Hsieh, M., Li, L., Liu, N., Tao, D., et al (2022). Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver. Physical Review Letters, 128(11). [More Information]
Ding, X., Wang, N., Zhang, S., Huang, Z., Li, X., Tang, M., Liu, T., Gao, X. (2022). Exploring Language Hierarchy for Video Grounding. IEEE Transactions on Image Processing, 31, 4693-4706. [More Information]
Yang, X., Deng, C., Liu, T., Tao, D. (2022). Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 1992-2003. [More Information]
Ju, L., Wang, X., Wang, L., Mahapatra, D., Zhao, X., Zhou, Q., Liu, T., Ge, Z. (2022). Improving Medical Images Classification with Label Noise Using Dual-Uncertainty Estimation. IEEE Transactions on Medical Imaging, 41(6), 1533-1546. [More Information]
Lan, L., Liu, T., Zhang, X., Xu, C., Luo, Z. (2022). Label Propagated Nonnegative Matrix Factorization for Clustering. IEEE Transactions On Knowledge And Data Engineering, 34(1), 340-351. [More Information]
Wu, S., Liu, T., Han, B., Yu, J., Niu, G., Sugiyama, M. (2022). Learning from Noisy Pairwise Similarity and Unlabeled Data. Journal of Machine Learning Research, 23.
Wu, Z., Xia, X., Wang, R., Li, J., Yu, J., Mao, Y., Liu, T. (2022). LR-SVM+: Learning Using Privileged Information with Noisy Labels. IEEE Transactions on Multimedia, 24, 1080-1092. [More Information]
Zhang, J., Liu, T., Tao, D. (2022). On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier. IEEE Transactions on Neural Networks and Learning Systems, 33(10), 5766-5774. [More Information]
Du, Y., Hsieh, M., Liu, T., You, S., Tao, D. (2022). Quantum Differentially Private Sparse Regression Learning. IEEE Transactions on Information Theory, 68(8), 5217-5233. [More Information]
Wang, H., Deng, C., Liu, T., Tao, D. (2022). Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 9181-9194. [More Information]
Zhang, Z., Li, M., Xie, H., Yu, J., Liu, T., Chen, C. (2022). TWGAN: Twin Discriminator Generative Adversarial Networks. IEEE Transactions on Multimedia, 24, 677-688. [More Information]
He, S., Wang, R., Liu, T., Yi, C., Jin, X., Liu, R., Zhou, W. (2022). Type-I Generative Adversarial Attack. IEEE Transactions on Dependable and Secure Computing. [More Information]
Pan, J., Chen, Z., He, Y., Liu, T., Cheng, X., Xiao, J., Feng, H. (2022). Why Controlling the Asymptomatic Infection Is Important: A Modelling Study with Stability and Sensitivity Analysis. Fractal and Fractional, 6(4). [More Information]
Du, Y., Hsieh, M., Liu, T., Tao, D. (2021). A Grover-search based quantum learning scheme for classification. New Journal of Physics, 23(2), 23020. [More Information]
Chen, Z., Ouyang, W., Liu, T., Tao, D. (2021). A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection. International Journal of Computer Vision, 129(4), 1121-1138. [More Information]
Zhang, J., Liu, T., Tao, D. (2021). An Optimal Transport Analysis on Generalization in Deep Learning. IEEE Transactions on Neural Networks and Learning Systems. [More Information]
Huang, H., Du, Y., Gong, M., Zhao, Y., Wu, Y., Wang, C., Li, S., Liang, F., Lin, J., Xu, Y., Liu, T., Tao, D., et al (2021). Experimental Quantum Generative Adversarial Networks for Image Generation. Physical Review Applied, 16(2), 24051. [More Information]
Shao, J., Du, B., Wu, C., Gong, M., Liu, T. (2021). HRSiam: High-Resolution Siamese Network, towards Space-Borne Satellite Video Tracking. IEEE Transactions on Image Processing, 30, 3056-3068. [More Information]
Gong, C., Wang, Q., Liu, T., Han, B., You, J., Yang, J., Tao, D. (2021). Instance-Dependent Positive and Unlabeled Learning with Labeling Bias Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8), 4163-4177. [More Information]
Ding, X., Wang, N., Gao, X., Li, J., Wang, X., Liu, T. (2021). KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization. IEEE Transactions on Image Processing, 30, 6869-6878. [More Information]
Ke, J., Gong, C., Liu, T., Zhao, L., Yang, J., Tao, D. (2021). Laplacian Welsch Regularization for Robust Semisupervised Learning. IEEE Transactions on Cybernetics, 52(1), 164-177. [More Information]
Du, Y., Hsieh, M., Liu, T., You, S., Tao, D. (2021). Learnability of Quantum Neural Networks. PRX QUANTUM, 2(4), 40337. [More Information]
Gong, C., Shi, H., Liu, T., Zhang, C., Yang, J., Tao, D. (2021). Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3), 918-932. [More Information]
Li, S., Jia, K., Wen, Y., Liu, T., Tao, D. (2021). Orthogonal Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(4), 1352-1368. [More Information]
Du, Y., Hsieh, M., Liu, T., Tao, D., Liu, N. (2021). Quantum noise protects quantum classifiers against adversaries. Physical Review Research, 3(2), 23153. [More Information]
Yao, Y., Yu, B., Gong, C., Liu, T. (2021). Understanding How Pretraining Regularizes Deep Learning Algorithms. IEEE Transactions on Neural Networks and Learning Systems. [More Information]
Liu, X., Wang, L., Zhu, X., Li, M., Zhu, E., Liu, T., Liu, L., Dou, Y., Yin, J. (2020). Absent Multiple Kernel Learning Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(6), 1303-1316. [More Information]
Yang, E., Liu, T., Deng, C., Tao, D. (2020). Adversarial Examples for Hamming Space Search. IEEE Transactions on Cybernetics, 50(4), 1473-1484. [More Information]
Du, Y., Hsieh, M., Liu, T., Tao, D. (2020). Expressive power of parametrized quantum circuits. Physical Review Research, 2(3), 33125. [More Information]
Ding, X., Wang, N., Gao, X., Li, J., Wang, X., Liu, T. (2020). Group Feedback Capsule Network. IEEE Transactions on Image Processing, 29, 6789-6799. [More Information]
Wei, Y., Gong, C., Chen, S., Liu, T., Yang, J., Tao, D. (2020). Harnessing Side Information for Classification under Label Noise. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3178-3192. [More Information]
Liu, X., Zhu, X., Li, M., Wang, L., Zhu, E., Liu, T., Kloft, M., Shen, D., Yin, J., Gao, W. (2020). Multiple Kernel k-Means with Incomplete Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(5), 1191-1204. [More Information]
Du, Y., Hsieh, M., Liu, T., Tao, D. (2020). Quantum-inspired algorithm for general minimum conical hull problems. Physical Review Research, 2(3), 33199. [More Information]
Lou, Y., Duan, L., Luo, Y., Chen, Z., Liu, T., Wang, S., Gao, W. (2020). Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm. IEEE Transactions on Multimedia, 22(11), 3002-3013. [More Information]
Deng, C., Yang, E., Liu, T., Tao, D. (2020). Two-Stream Deep Hashing with Class-Specific Centers for Supervised Image Search. IEEE Transactions on Neural Networks and Learning Systems, 31(6), 2189-2201. [More Information]
He, F., Liu, T., Tao, D. (2020). Why ResNet Works? Residuals Generalize. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5349-5362. [More Information]
Lei, T., Jia, X., Liu, T., Liu, S., Meng, H., Nandi, A. (2019). Adaptive Morphological Reconstruction for Seeded Image Segmentation. IEEE Transactions on Image Processing, 28(11), 5510-5523. [More Information]
Tian, X., Li, Y., Liu, T., Wang, X., Tao, D. (2019). Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space. IEEE Transactions on Neural Networks and Learning Systems, 30(6), 1818-1830. [More Information]
Wang, Q., Yu, J., Liu, T., Liu, W. (2019). Guest editorial: Visual domain adaptation and generalisation. IET Computer Vision, 13(2), 87-89. [More Information]
Gong, C., Liu, T., Yang, J., Tao, D. (2019). Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3471-3483. [More Information]
Luo, Y., Wen, Y., Liu, T., Tao, D. (2019). Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4), 1013-1026. [More Information]
Guan, N., Liu, T., Zhang, Y., Tao, D., Davis, L. (2019). Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1), 246-259. [More Information]
Deng, C., Yang, E., Liu, T., Li, J., Liu, W., Tao, D. (2019). Unsupervised Semantic-Preserving Adversarial Hashing for Image Search. IEEE Transactions on Image Processing, 28(8), 4032-4044. [More Information]
Gong, C., Liu, T., Tang, Y., Yang, J., Yang, J., Tao, D. (2018). A Regularization Approach for Instance-Based Superset Label Learning. IEEE Transactions on Cybernetics, 48(3), 967-978. [More Information]
Shen, X., Tian, X., Liu, T., Xu, F., Tao, D. (2018). Continuous Dropout. IEEE Transactions on Neural Networks and Learning Systems, 29(9), 3926-3937. [More Information]
Ma, K., Fu, H., Liu, T., Wang, Z., Tao, D. (2018). Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks. IEEE Transactions on Image Processing, 27(10), 5155-5166. [More Information]
Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T. (2018). Fast Supervised Discrete Hashing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2), 490-496. [More Information]
Wang, R., Liu, T., Tao, D. (2018). Multiclass Learning with Partially Corrupted Labels. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2568-2580. [More Information]
Li, Y., Tian, X., Liu, T., Tao, D. (2018). On better exploring and exploiting task relationships in multitask learning: Joint model and feature learning. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1975-1985. [More Information]
Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T. (2018). Supervised Discrete Hashing with Relaxation. IEEE Transactions on Neural Networks and Learning Systems, 29(3), 608-617. [More Information]
Liu, T., Tao, D., Song, M., Maybank, S. (2017). Algorithm-Dependent Generalization Bounds for Multi-Task Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(2), 227-241. [More Information]
Ma, K., Liu, W., Liu, T., Wang, Z., Tao, D. (2017). dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs. IEEE Transactions on Image Processing, 26(8), 3951-3964. [More Information]
Liu, Q., Sun, Y., Wang, C., Liu, T., Tao, D. (2017). Elastic net hypergraph learning for image clustering and semi-supervised classification. IEEE Transactions on Image Processing, 26(1), 452-463. [More Information]
Zhang, Y., Du, B., Zhang, L., Liu, T. (2017). Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 894-906. [More Information]
Liu, T., Gong, M., Tao, D. (2017). Large-Cone Nonnegative Matrix Factorization. IEEE Transactions on Neural Networks and Learning Systems, 28(9), 2129-2142. [More Information]
Liu, H., Wu, J., Liu, T., Tao, D., Fu, Y. (2017). Spectral Ensemble Clustering via Weighted K-means: Theoretical and Practical Evidence. IEEE Transactions On Knowledge And Data Engineering, 29(5), 1129-1143. [More Information]
Liu, T., Tao, D. (2016). Classification with Noisy Labels by Importance Reweighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 447-461. [More Information]
Liu, T., Tao, D., Xu, D. (2016). Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes. Neural Computation, 28(10), 2213-2249. [More Information]
Xiong, H., Liu, T., Tao, D., Shen, H. (2016). Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repairing. IEEE Transactions on Image Processing, 25(8), 3626-3637. [More Information]
Xu, C., Liu, T., Tao, D., Xu, C. (2016). Local Rademacher Complexity for Multi-Label Learning. IEEE Transactions on Image Processing, 25(3), 1495-1507. [More Information]
Liu, T., Tao, D. (2016). On the performance of Manhattan nonnegative matrix factorization. IEEE Transactions on Neural Networks and Learning Systems, 27(9), 1851-1863. [More Information]
Gui, J., Liu, T., Tao, D., Sun, Z., Tan, T. (2016). Representative Vector Machines: A Unified Framework for Classical Classifiers. IEEE Transactions on Cybernetics, 46(8), 1877-1888. [More Information]
Li, X., Liu, T., Deng, J., Tao, D. (2016). Video face editing using temporal-spatial-smooth warping. ACM Transactions on Intelligent Systems and Technology, 7(3), 1-28. [More Information]
Gong, C., Liu, T., Tao, D., Fu, K., Tu, E., Yang, J. (2015). Deformed Graph Laplacian for Semisupervised Learning. IEEE Transactions on Neural Networks and Learning Systems, 26(10), 2261-2274. [More Information]
Luo, Y., Liu, T., Tao, D., Xu, C. (2015). Multiview matrix completion for multilabel image classification. IEEE Transactions on Image Processing, 24(8), 2355-2368. [More Information]
Lu, Y., Xie, F., Liu, T., Jiang, Z., Tao, D. (2015). No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model. IEEE Signal Processing Letters, 22(10), 1811-1815. [More Information]
Luo, Y., Liu, T., Tao, D., Xu, C. (2014). Decomposition-based transfer distance metric learning for image classification. IEEE Transactions on Image Processing, 23(9), 3789-3801. [More Information]

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Conferences

Zhou, D., Wang, N., Yang, H., Gao, X., Liu, T. (2023). Phase-aware Adversarial Defense for Improving Adversarial Robustness. 40th International Conference on Machine Learning, ICML 2023, NA: ML Research Press.
Kim, J., Liu, T., Yacef, K. (2022). Improving Supervised Learning in Conversational Analysis through Reusing Preprocessing Data as Auxiliary Supervisors. ACM International Conference Proceeding Series, : SPIE.
An,, X., Deng,, J., Guo,, J., Feng,, Z., Zhu,, X., Yang,, J., Liu, T. (2022). Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, : IEEE Computer Society.
Sugiyama,, M., Liu, T., Han,, B., Liu,, Y., Niu,, G. (2022). Learning and Mining with Noisy Labels. International Conference on Information and Knowledge Management, Proceedings, : Springer Verlag.
Yang,, E., Yao,, D., Liu, T., Deng,, C. (2022). Mutual Quantization for Cross-Modal Search with Noisy Labels. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, : IEEE Computer Society.
Luo,, Y., Duan,, L., Bai,, Y., Liu, T., Lou,, Y., Wen,, Y. (2022). Nonlinear Multi-Model Reuse. 2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022, : Institute of Electrical and Electronics Engineers Inc.
Xia, X., Shan,, S., Gong,, M., Wang,, N., Gao,, F., Wei,, H., Liu, T. (2022). Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, : Springer Verlag.
Li,, S., Xia, X., Ge,, S., Liu, T. (2022). Selective-Supervised Contrastive Learning with Noisy Labels. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, : IEEE Computer Society.
Guo,, X., Liu,, J., Liu, T., Yuan,, Y. (2022). SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, : IEEE Computer Society.
Pasdar,, A., Lee,, Y., Liu, T., Hong, S. (2022). Train Me to Fight: Machine-Learning Based On-Device Malware Detection for Mobile Devices. Proceedings - 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022, : Springer Verlag.
Cai, S., Hong, S., Shen, J., Liu, T. (2021). A Machine Learning Approach for Predicting Human Preference for Graph Layouts. 14th IEEE Pacific Visualization Symposium (PacificVis 2021), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Zhu, Z., Liu, T., Liu, Y. (2021). A Second-Order Approach to Learning with Instance-Dependent Label Noise. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Yu, J., Hao, X., Cui, Z., He, P., Liu, T. (2021). Boosting Fairness for Masked Face Recognition. 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Dong,, J., Fang,, Z., Liu,, A., Sun,, G., Liu, T. (2021). Confident-Anchor-Induced Multi-Source-Free Domain Adaptation. NeurIPS 2021, Virtual, Online: Springer Science and Business Media Deutschland GmbH.
Yao, Y., Liu, T., Gong,, M., Han,, B., Niu,, G., Zhang,, K. (2021). Instance-Dependent Label-Noise Learning under Structural Causal Models. NeurIPS 2021, Virtual, Online: Springer Science and Business Media Deutschland GmbH.
Wang, Q., Yao, J., Gong, C., Liu, T., Gong, M., Yang, H., Han, B. (2021). Learning with Group Noise. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Palo Alto, California: AAAI Press.
Wang,, Q., Liu,, F., Han,, B., Liu, T., Gong,, C., Niu,, G., Zhou,, M., Sugiyama,, M. (2021). Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021, Virtual, Online: Springer Science and Business Media Deutschland GmbH.
Ju, L., Wang, X., Wang, L., Liu, T., Zhao, X., Drummond, T., Mahapatra, D., Ge, Z. (2021). Relational Subsets Knowledge Distillation for Long-Tailed Retinal Diseases Recognition. 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Cham: Springer Nature. [More Information]
Zhou,, D., Wang,, N., Peng,, C., Gao,, X., Wang,, X., Yu,, J., Liu, T. (2021). Removing Adversarial Noise in Class Activation Feature Space. 18th IEEE/CVF International Conference on Computer Vision (ICCV 2021), : Springer International Publishing AG.
Huang, Z., Shen, X., Xing, J., Liu, T., Tian, X., Li, H., Deng, B., Huang, J., Hua, X. (2021). Revisiting Knowledge Distillation: An Inheritance and Exploration Framework. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
He, J., Khushi, M., Tran, N., Liu, T. (2021). Robust Dual Recurrent Neural Networks for Financial Time Series Prediction. SIAM International Conference on Data Mining (SDM21), Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM). [More Information]
Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z., Chang, Y. (2021). ROBUST EARLY-LEARNING: HINDERING THEMEMORIZATION OF NOISY LABELS. The Ninth International Conference on Learning Representations (ICLR 2021), La Jolla, CA: International Conference on Representation Learning (ICLR). [More Information]
Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z., Chang, Y. (2021). Robust early-learning: Hindering the memorization of noisy labels. The Ninth International Conference on Learning Representations (ICLR 2021), La Jolla, CA: International Conference on Representation Learning (ICLR).
Furusho, Y., Liu, T., Ikeda, K. (2021). Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer. 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, Switzerland: Springer Cham.
Wang, Q., Han, B., Liu, T., Niu, G., Yang, J., Gong, C. (2021). Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Palo Alto, California: AAAI Press.
Gan, S., Luo, Y., Wen, Y., Liu, T., Hu, H. (2020). Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval. 28th ACM Multimedia Conference (MM 2020), New York: Association for Computing Machinery (ACM). [More Information]
Qiao, M., Yu, J., Liu, T., Wang, X., Tao, D. (2020). Diversified Bayesian Nonnegative Matrix Factorization. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), Palo Alto: AAAI Press. [More Information]
Zhao, S., Gong, M., Liu, T., Fu, H., Tao, D. (2020). Domain generalization via entropy regularization. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS).
Yao, Y., Liu, T., Han, B., Gong, M., Deng, J., Niu, G., Sugiyama, M. (2020). Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS).
Yao, Y., Liu, T., Han, B., Gong, M., Deng, J., Niu, G., Sugiyama, M. (2020). Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS). [More Information]
Zhang, Y., Li, Y., Liu, T., Tian, X. (2020). Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Xu, Y., Gong, M., Chen, J., Liu, T., Zhang, K., Batmanghelich, K. (2020). Generative-Discriminative Complementary Learning. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), Palo Alto: AAAI Press. [More Information]
Yu, X., Liu, T., Gong, M., Zhang, K., Batmanghelich, K., Tao, D. (2020). Label-noise robust domain adaptation. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Cheng, J., Liu, T., Ramamohanarao, K., Tao, D. (2020). Learning with Bounded Instance- and Label-dependent Label Noise. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Guo, J., Gong, M., Liu, T., Zhang, K., Tao, D. (2020). LTF: A Label Transformation Framework for Correcting Target Shift. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Xia, X., Liu, T., Han, B., Wang, N., Gong, M., Liu, H., Niu, G., Tao, D., Sugiyama, M. (2020). Part-dependent Label Noise: Towards Instance-dependent Label Noise. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS).
Xia, X., Liu, T., Han, B., Wang, N., Gong, M., Liu, H., Niu, G., Tao, D., Sugiyama, M. (2020). Part-dependent Label Noise:Towards Instance-dependent Label Noise. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS). [More Information]
Deng, J., Guo, J., Liu, T., Gong, M., Zafeiriou, S. (2020). Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces. 16th European Conference on Computer Vision (ECCV 2020), Cham: Springer. [More Information]
Xia, X., Liu, T., Wang, N., Han, B., Gong, C., Niu, G., Sugiyma, M. (2019). Are Anchor Points Really Indispensable in Label-Noise Learning. 33th Conference on Neural Information Processing Systems (NeurIPS 2019), Canada: Neural Information Processing Systems Foundation.
He, F., Liu, T., Tao, D. (2019). Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. 33th Conference on Neural Information Processing Systems (NeurIPS 2019), Canada: Neural Information Processing Systems Foundation.
Yang, E., Liu, T., Deng, C., Liu, W., Tao, D. (2019). DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Zhang, C., Ren, D., Liu, T., Yang, J., Gong, C. (2019). Positive and Unlabeled Learning with Label Disambiguation. 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao: International Joint Conferences on Artificial Intelligence. [More Information]
Xu, Y., Gong, M., Liu, T., Batmanghelich, K., Wang, C. (2019). Robust Angular Local Descriptor Learning. 14th Asian Conference on Computer Vision (ACCV 2018), Cham: Springer. [More Information]
Lou, Y., Duan, L., Luo, Y., Chen, Z., Liu, T., Wang, S., Gao, W. (2019). Towards digital retina in smart cities: A model generation, utilization and communication paradigm. 2019 IEEE International Conference on Multimedia and Expo (ICME 2019), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Yu, X., Liu, T., Gong, M., Batmanghelich, K., Tao, D. (2018). An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, Utah: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Yu, B., Liu, T., Gong, M., Ding, C., Tao, D. (2018). Correcting the triplet selection bias for triplet loss. 15th European Conference on Computer Vision (ECCV2018), Cham: Springer. [More Information]
Li, Y., Tian, X., Gong, M., Liu, Y., Liu, T., Zhang, K., Tao, D. (2018). Deep domain generalization via conditional invariant adversarial networks. 15 th European Conference on Computer Vision ECCV 2018, Cham: Springer. [More Information]
Li, Y., Gong, M., Tian, X., Liu, T., Tao, D. (2018). Domain Generalization via Conditional Invariant Representations. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), Palo Alto: AAAI Press.
Yu, X., Liu, T., Gong, M., Tao, D. (2018). Learning with Biased Complementary Labels. 15th European Conference on Computer Vision ECCV 2018, Cham: Springer. [More Information]
Luo, Y., Liu, T., Wen, Y., Tao, D. (2018). Online heterogeneous transfer metric learning. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm: International Joint Conferences on Artificial Intelligence. [More Information]
Du, Y., Liu, T., Li, Y., Duan, R., Tao, D. (2018). Quantum divide-and-conquer anchoring for separable non-negative matrix factorization. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm: International Joint Conferences on Artificial Intelligence. [More Information]
Yang, E., Deng, C., Liu, T., Liu, W., Tao, D. (2018). Semantic structure-based unsupervised deep hashing. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm: International Joint Conferences on Artificial Intelligence. [More Information]
Liu, T., Lugosi, G., Neu, G., Tao, D. (2017). Algorithmic stability and hypothesis complexity. The 34th International Conference on Machine Learning, (ICML 2017), online: Proceedings of Machine Learning Research.
Luo, Y., Wen, Y., Liu, T., Tao, D. (2017). General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer. 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne: International Joint Conferences on Artificial Intelligence. [More Information]
Yu, X., Liu, T., Wang, X., Tao, D. (2017). On Compressing Deep Models by Low Rank and Sparse Decomposition. 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Liu, T., Yang, Q., Tao, D. (2017). Understanding How Feature Structure Transfers in Transfer Learning. 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne: International Joint Conferences on Artificial Intelligence. [More Information]
Xiong, H., Liu, T., Tao, D. (2016). Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair. 30th AAAI Conference on Artificial Intelligence (AAAI 2016), United States: AAAI Press.
Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., Scholkopf, B. (2016). Domain Adaptation with Conditional Transferable Components. 33rd International Conference on Machine Learning (ICML 2016), Stroudsburg: International Machine Learning Society.
Li, Y., Tian, X., Liu, T., Tao, D. (2015). Multi-task model and feature joint learning. 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires: AAAI Press.
Liu, H., Liu, T., Wu, J., Tao, D., Fu, Y. (2015). Spectral ensemble clustering. The 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), New York: Association for Computing Machinery (ACM). [More Information]
Shao, M., Li, S., Liu, T., Tao, D., Huang, T., Fu, Y. (2014). Learning relative features through adaptive pooling for image classification. 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW 2014), Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Liu, T., Tao, D. (2014). On the robustness and generalization of Cauchy regression. 2014 4th IEEE International Conference on Information Science and Technology (ICIST 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

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2024

Wu, S., Zhou, T., Du, Y., Yu, J., Han, B., Liu, T. (2024). A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Zhang, J., Song, B., Wang, H., Han, B., Liu, T., Liu, L., Sugiyama, M. (2024). BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(6), 4398-4409. [More Information]
Li, M., Zhou, T., Han, B., Liu, T., Liang, X., Zhao, J., Gong, C. (2024). Class-wise Contrastive Prototype Learning for Semi-Supervised Classification under Intersectional Class Mismatch. IEEE Transactions on Multimedia. [More Information]
Yang, E., Wang, Z., Shen, L., Yin, N., Liu, T., Guo, G., Wang, X., Tao, D. (2024). Continual Learning From a Stream of APIs. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Fu, S., Ma, X., Zhan, Y., You, F., Peng, Q., Liu, T., Bailey, J., Mandic, D. (2024). Finding core labels for maximizing generalization of graph neural networks. Neural Networks, 180, 106635. [More Information]
Yang, Y., Lin, C., Li, Q., Zhao, Z., Fan, H., Zhou, D., Wang, N., Liu, T., Shen, C. (2024). Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization. IEEE Transactions on Information Forensics and Security. [More Information]
Wang, J., Xia, X., Lan, L., Wu, X., Yu, J., Yang, W., Han, B., Liu, T. (2024). Tackling Noisy Labels With Network Parameter Additive Decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Liu, L., Wang, N., Liu, D., Yang, X., Gao, X., Liu, T. (2024). Towards Specific Domain Prompt Learning Via Improved Text Label Optimization. IEEE Transactions on Multimedia. [More Information]

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2023

Yang, S., Wu, S., Yang, E., Han, B., Liu, Y., Xu, M., Niu, G., Liu, T. (2023). A Parametrical Model for Instance-Dependent Label Noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 14055-14068. [More Information]
Li, X., Xia, X., Zhu, F., Liu, T., Zhang, X., Liu, C. (2023). Dynamics-aware loss for learning with label noise. Pattern Recognition, 144, 109835. [More Information]
Xia, X., Han, B., Wang, N., Deng, J., Li, J., Mao, Y., Liu, T. (2023). Extended T: Learning With Mixed Closed-Set and Open-Set Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3047-3058. [More Information]
Zhang, J., Liu, T., Tao, D. (2023). Going Deeper, Generalizing Better: An Information-Theoretic View for Deep Learning. IEEE Transactions on Neural Networks and Learning Systems. [More Information]
Guo, X., Liu, J., Liu, T., Yuan, Y. (2023). Handling Open-set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, , 1-16. [More Information]
Wang, T., Shen, L., Fan, Q., Xu, T., Liu, T., Xiong, H. (2023). Joint Admission Control and Resource Allocation of Virtual Network Embedding Via Hierarchical Deep Reinforcement Learning. IEEE Transactions on Services Computing, , 1-14. [More Information]
Liu, C., Zhan, Y., Yu, B., Liu, L., Du, B., Hu, W., Liu, T. (2023). On exploring node-feature and graph-structure diversities for node drop graph pooling. Neural Networks, 167, 559-571. [More Information]
Chan, A., Wu, S., Vernon, S., Tang, O., Figtree, G., Liu, T., Yang, J., Patrick, E. (2023). Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk. iScience, 26(5), Article 106633 - 1-Article 106633 - 17. [More Information]
Zhou, D., Wang, N., Yang, H., Gao, X., Liu, T. (2023). Phase-aware Adversarial Defense for Improving Adversarial Robustness. 40th International Conference on Machine Learning, ICML 2023, NA: ML Research Press.
Shen, J., Yao, Y., Huang, S., Wang, Z., Zhang, J., Wang, R., Yu, J., Liu, T. (2023). ProtoSimi: label correction for fine-grained visual categorization. Machine Learning. [More Information]
Tian, J., Wu, X., Hsieh, M., Liu, T., Yang, W., Tao, D., Sun, X., Du, Y., Zhao, S., Liu, Q., Zhang, K., et al (2023). Recent Advances for Quantum Neural Networks in Generative Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12321-12340. [More Information]
Xia, X., Lu, P., Gong, C., Han, B., Yu, J., Yu, J., Liu, T. (2023). Regularly Truncated M-Estimators for Learning With Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence. [More Information]
Ma, J., Liu, J., Wang, Y., Li, J., Liu, T. (2023). Relation-Aware Fine-Grained Reasoning Network for Textbook Question Answering. IEEE Transactions on Neural Networks and Learning Systems, 34(1), 15-27. [More Information]
Li, S., Liu, T., Tan, J., Zeng, D., Ge, S. (2023). Trustable Co-Label Learning From Multiple Noisy Annotators. IEEE Transactions on Multimedia, 25, 1045-1057. [More Information]

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2022

Cai, S., Hong, S., Shen, J., Liu, T. (2022). A Machine Learning Approach for Predicting Human Preference for Graph Drawings. Journal of Graph Algorithms and Applications, 26(4), 447-470. [More Information]
Cai, S., Hong, S., Xia, X., Liu, T., Huang, W. (2022). A machine learning approach for predicting human shortest path task performance. Visual Informatics, 6(2), 50-61. [More Information]
Yang, S., Wu, S., Liu, T., Xu, M. (2022). Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 9830-9843. [More Information]
Bao, G., Chen, H., Liu, T., Gong, G., Yin, Y., Wang, L., Wang, X. (2022). COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. Pattern Recognition, 124, 108499. [More Information]
Yin, X., Du, Y., Fei, Y., Zhang, R., Liu, L., Mao, Y., Liu, T., Hsieh, M., Li, L., Liu, N., Tao, D., et al (2022). Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver. Physical Review Letters, 128(11). [More Information]
Ding, X., Wang, N., Zhang, S., Huang, Z., Li, X., Tang, M., Liu, T., Gao, X. (2022). Exploring Language Hierarchy for Video Grounding. IEEE Transactions on Image Processing, 31, 4693-4706. [More Information]
Yang, X., Deng, C., Liu, T., Tao, D. (2022). Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 1992-2003. [More Information]
Ju, L., Wang, X., Wang, L., Mahapatra, D., Zhao, X., Zhou, Q., Liu, T., Ge, Z. (2022). Improving Medical Images Classification with Label Noise Using Dual-Uncertainty Estimation. IEEE Transactions on Medical Imaging, 41(6), 1533-1546. [More Information]
Kim, J., Liu, T., Yacef, K. (2022). Improving Supervised Learning in Conversational Analysis through Reusing Preprocessing Data as Auxiliary Supervisors. ACM International Conference Proceeding Series, : SPIE.
An,, X., Deng,, J., Guo,, J., Feng,, Z., Zhu,, X., Yang,, J., Liu, T. (2022). Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, : IEEE Computer Society.
Lan, L., Liu, T., Zhang, X., Xu, C., Luo, Z. (2022). Label Propagated Nonnegative Matrix Factorization for Clustering. IEEE Transactions On Knowledge And Data Engineering, 34(1), 340-351. [More Information]
Sugiyama,, M., Liu, T., Han,, B., Liu,, Y., Niu,, G. (2022). Learning and Mining with Noisy Labels. International Conference on Information and Knowledge Management, Proceedings, : Springer Verlag.
Wu, S., Liu, T., Han, B., Yu, J., Niu, G., Sugiyama, M. (2022). Learning from Noisy Pairwise Similarity and Unlabeled Data. Journal of Machine Learning Research, 23.
Wu, Z., Xia, X., Wang, R., Li, J., Yu, J., Mao, Y., Liu, T. (2022). LR-SVM+: Learning Using Privileged Information with Noisy Labels. IEEE Transactions on Multimedia, 24, 1080-1092. [More Information]
Yang,, E., Yao,, D., Liu, T., Deng,, C. (2022). Mutual Quantization for Cross-Modal Search with Noisy Labels. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, : IEEE Computer Society.
Luo,, Y., Duan,, L., Bai,, Y., Liu, T., Lou,, Y., Wen,, Y. (2022). Nonlinear Multi-Model Reuse. 2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022, : Institute of Electrical and Electronics Engineers Inc.
Zhang, J., Liu, T., Tao, D. (2022). On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier. IEEE Transactions on Neural Networks and Learning Systems, 33(10), 5766-5774. [More Information]
Du, Y., Hsieh, M., Liu, T., You, S., Tao, D. (2022). Quantum Differentially Private Sparse Regression Learning. IEEE Transactions on Information Theory, 68(8), 5217-5233. [More Information]
Xia, X., Shan,, S., Gong,, M., Wang,, N., Gao,, F., Wei,, H., Liu, T. (2022). Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, : Springer Verlag.
Li,, S., Xia, X., Ge,, S., Liu, T. (2022). Selective-Supervised Contrastive Learning with Noisy Labels. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, : IEEE Computer Society.
Guo,, X., Liu,, J., Liu, T., Yuan,, Y. (2022). SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, : IEEE Computer Society.
Pasdar,, A., Lee,, Y., Liu, T., Hong, S. (2022). Train Me to Fight: Machine-Learning Based On-Device Malware Detection for Mobile Devices. Proceedings - 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022, : Springer Verlag.
Wang, H., Deng, C., Liu, T., Tao, D. (2022). Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 9181-9194. [More Information]
Zhang, Z., Li, M., Xie, H., Yu, J., Liu, T., Chen, C. (2022). TWGAN: Twin Discriminator Generative Adversarial Networks. IEEE Transactions on Multimedia, 24, 677-688. [More Information]
He, S., Wang, R., Liu, T., Yi, C., Jin, X., Liu, R., Zhou, W. (2022). Type-I Generative Adversarial Attack. IEEE Transactions on Dependable and Secure Computing. [More Information]
Pan, J., Chen, Z., He, Y., Liu, T., Cheng, X., Xiao, J., Feng, H. (2022). Why Controlling the Asymptomatic Infection Is Important: A Modelling Study with Stability and Sensitivity Analysis. Fractal and Fractional, 6(4). [More Information]

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2021

Du, Y., Hsieh, M., Liu, T., Tao, D. (2021). A Grover-search based quantum learning scheme for classification. New Journal of Physics, 23(2), 23020. [More Information]
Cai, S., Hong, S., Shen, J., Liu, T. (2021). A Machine Learning Approach for Predicting Human Preference for Graph Layouts. 14th IEEE Pacific Visualization Symposium (PacificVis 2021), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Zhu, Z., Liu, T., Liu, Y. (2021). A Second-Order Approach to Learning with Instance-Dependent Label Noise. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Chen, Z., Ouyang, W., Liu, T., Tao, D. (2021). A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection. International Journal of Computer Vision, 129(4), 1121-1138. [More Information]
Zhang, J., Liu, T., Tao, D. (2021). An Optimal Transport Analysis on Generalization in Deep Learning. IEEE Transactions on Neural Networks and Learning Systems. [More Information]
Yu, J., Hao, X., Cui, Z., He, P., Liu, T. (2021). Boosting Fairness for Masked Face Recognition. 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Dong,, J., Fang,, Z., Liu,, A., Sun,, G., Liu, T. (2021). Confident-Anchor-Induced Multi-Source-Free Domain Adaptation. NeurIPS 2021, Virtual, Online: Springer Science and Business Media Deutschland GmbH.
Huang, H., Du, Y., Gong, M., Zhao, Y., Wu, Y., Wang, C., Li, S., Liang, F., Lin, J., Xu, Y., Liu, T., Tao, D., et al (2021). Experimental Quantum Generative Adversarial Networks for Image Generation. Physical Review Applied, 16(2), 24051. [More Information]
Shao, J., Du, B., Wu, C., Gong, M., Liu, T. (2021). HRSiam: High-Resolution Siamese Network, towards Space-Borne Satellite Video Tracking. IEEE Transactions on Image Processing, 30, 3056-3068. [More Information]
Yao, Y., Liu, T., Gong,, M., Han,, B., Niu,, G., Zhang,, K. (2021). Instance-Dependent Label-Noise Learning under Structural Causal Models. NeurIPS 2021, Virtual, Online: Springer Science and Business Media Deutschland GmbH.
Gong, C., Wang, Q., Liu, T., Han, B., You, J., Yang, J., Tao, D. (2021). Instance-Dependent Positive and Unlabeled Learning with Labeling Bias Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8), 4163-4177. [More Information]
Ding, X., Wang, N., Gao, X., Li, J., Wang, X., Liu, T. (2021). KFC: An Efficient Framework for Semi-Supervised Temporal Action Localization. IEEE Transactions on Image Processing, 30, 6869-6878. [More Information]
Ke, J., Gong, C., Liu, T., Zhao, L., Yang, J., Tao, D. (2021). Laplacian Welsch Regularization for Robust Semisupervised Learning. IEEE Transactions on Cybernetics, 52(1), 164-177. [More Information]
Du, Y., Hsieh, M., Liu, T., You, S., Tao, D. (2021). Learnability of Quantum Neural Networks. PRX QUANTUM, 2(4), 40337. [More Information]
Wang, Q., Yao, J., Gong, C., Liu, T., Gong, M., Yang, H., Han, B. (2021). Learning with Group Noise. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Palo Alto, California: AAAI Press.
Gong, C., Shi, H., Liu, T., Zhang, C., Yang, J., Tao, D. (2021). Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3), 918-932. [More Information]
Li, S., Jia, K., Wen, Y., Liu, T., Tao, D. (2021). Orthogonal Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(4), 1352-1368. [More Information]
Wang,, Q., Liu,, F., Han,, B., Liu, T., Gong,, C., Niu,, G., Zhou,, M., Sugiyama,, M. (2021). Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021, Virtual, Online: Springer Science and Business Media Deutschland GmbH.
Du, Y., Hsieh, M., Liu, T., Tao, D., Liu, N. (2021). Quantum noise protects quantum classifiers against adversaries. Physical Review Research, 3(2), 23153. [More Information]
Ju, L., Wang, X., Wang, L., Liu, T., Zhao, X., Drummond, T., Mahapatra, D., Ge, Z. (2021). Relational Subsets Knowledge Distillation for Long-Tailed Retinal Diseases Recognition. 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Cham: Springer Nature. [More Information]
Zhou,, D., Wang,, N., Peng,, C., Gao,, X., Wang,, X., Yu,, J., Liu, T. (2021). Removing Adversarial Noise in Class Activation Feature Space. 18th IEEE/CVF International Conference on Computer Vision (ICCV 2021), : Springer International Publishing AG.
Huang, Z., Shen, X., Xing, J., Liu, T., Tian, X., Li, H., Deng, B., Huang, J., Hua, X. (2021). Revisiting Knowledge Distillation: An Inheritance and Exploration Framework. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
He, J., Khushi, M., Tran, N., Liu, T. (2021). Robust Dual Recurrent Neural Networks for Financial Time Series Prediction. SIAM International Conference on Data Mining (SDM21), Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM). [More Information]
Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z., Chang, Y. (2021). ROBUST EARLY-LEARNING: HINDERING THEMEMORIZATION OF NOISY LABELS. The Ninth International Conference on Learning Representations (ICLR 2021), La Jolla, CA: International Conference on Representation Learning (ICLR). [More Information]
Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z., Chang, Y. (2021). Robust early-learning: Hindering the memorization of noisy labels. The Ninth International Conference on Learning Representations (ICLR 2021), La Jolla, CA: International Conference on Representation Learning (ICLR).
Furusho, Y., Liu, T., Ikeda, K. (2021). Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer. 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, Switzerland: Springer Cham.
Wang, Q., Han, B., Liu, T., Niu, G., Yang, J., Gong, C. (2021). Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Palo Alto, California: AAAI Press.
Yao, Y., Yu, B., Gong, C., Liu, T. (2021). Understanding How Pretraining Regularizes Deep Learning Algorithms. IEEE Transactions on Neural Networks and Learning Systems. [More Information]

show 25 more

2020

Liu, X., Wang, L., Zhu, X., Li, M., Zhu, E., Liu, T., Liu, L., Dou, Y., Yin, J. (2020). Absent Multiple Kernel Learning Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(6), 1303-1316. [More Information]
Yang, E., Liu, T., Deng, C., Tao, D. (2020). Adversarial Examples for Hamming Space Search. IEEE Transactions on Cybernetics, 50(4), 1473-1484. [More Information]
Gan, S., Luo, Y., Wen, Y., Liu, T., Hu, H. (2020). Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval. 28th ACM Multimedia Conference (MM 2020), New York: Association for Computing Machinery (ACM). [More Information]
Qiao, M., Yu, J., Liu, T., Wang, X., Tao, D. (2020). Diversified Bayesian Nonnegative Matrix Factorization. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), Palo Alto: AAAI Press. [More Information]
Zhao, S., Gong, M., Liu, T., Fu, H., Tao, D. (2020). Domain generalization via entropy regularization. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS).
Yao, Y., Liu, T., Han, B., Gong, M., Deng, J., Niu, G., Sugiyama, M. (2020). Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS).
Yao, Y., Liu, T., Han, B., Gong, M., Deng, J., Niu, G., Sugiyama, M. (2020). Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS). [More Information]
Zhang, Y., Li, Y., Liu, T., Tian, X. (2020). Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Du, Y., Hsieh, M., Liu, T., Tao, D. (2020). Expressive power of parametrized quantum circuits. Physical Review Research, 2(3), 33125. [More Information]
Xu, Y., Gong, M., Chen, J., Liu, T., Zhang, K., Batmanghelich, K. (2020). Generative-Discriminative Complementary Learning. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), Palo Alto: AAAI Press. [More Information]
Ding, X., Wang, N., Gao, X., Li, J., Wang, X., Liu, T. (2020). Group Feedback Capsule Network. IEEE Transactions on Image Processing, 29, 6789-6799. [More Information]
Wei, Y., Gong, C., Chen, S., Liu, T., Yang, J., Tao, D. (2020). Harnessing Side Information for Classification under Label Noise. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3178-3192. [More Information]
Yu, X., Liu, T., Gong, M., Zhang, K., Batmanghelich, K., Tao, D. (2020). Label-noise robust domain adaptation. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Cheng, J., Liu, T., Ramamohanarao, K., Tao, D. (2020). Learning with Bounded Instance- and Label-dependent Label Noise. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Guo, J., Gong, M., Liu, T., Zhang, K., Tao, D. (2020). LTF: A Label Transformation Framework for Correcting Target Shift. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Liu, X., Zhu, X., Li, M., Wang, L., Zhu, E., Liu, T., Kloft, M., Shen, D., Yin, J., Gao, W. (2020). Multiple Kernel k-Means with Incomplete Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(5), 1191-1204. [More Information]
Xia, X., Liu, T., Han, B., Wang, N., Gong, M., Liu, H., Niu, G., Tao, D., Sugiyama, M. (2020). Part-dependent Label Noise: Towards Instance-dependent Label Noise. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS).
Xia, X., Liu, T., Han, B., Wang, N., Gong, M., Liu, H., Niu, G., Tao, D., Sugiyama, M. (2020). Part-dependent Label Noise:Towards Instance-dependent Label Noise. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), San Diego: Neural Information Processing Systems (NIPS). [More Information]
Du, Y., Hsieh, M., Liu, T., Tao, D. (2020). Quantum-inspired algorithm for general minimum conical hull problems. Physical Review Research, 2(3), 33199. [More Information]
Deng, J., Guo, J., Liu, T., Gong, M., Zafeiriou, S. (2020). Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces. 16th European Conference on Computer Vision (ECCV 2020), Cham: Springer. [More Information]
Lou, Y., Duan, L., Luo, Y., Chen, Z., Liu, T., Wang, S., Gao, W. (2020). Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm. IEEE Transactions on Multimedia, 22(11), 3002-3013. [More Information]
Deng, C., Yang, E., Liu, T., Tao, D. (2020). Two-Stream Deep Hashing with Class-Specific Centers for Supervised Image Search. IEEE Transactions on Neural Networks and Learning Systems, 31(6), 2189-2201. [More Information]
He, F., Liu, T., Tao, D. (2020). Why ResNet Works? Residuals Generalize. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5349-5362. [More Information]

show 20 more

2019

Lei, T., Jia, X., Liu, T., Liu, S., Meng, H., Nandi, A. (2019). Adaptive Morphological Reconstruction for Seeded Image Segmentation. IEEE Transactions on Image Processing, 28(11), 5510-5523. [More Information]
Xia, X., Liu, T., Wang, N., Han, B., Gong, C., Niu, G., Sugiyma, M. (2019). Are Anchor Points Really Indispensable in Label-Noise Learning. 33th Conference on Neural Information Processing Systems (NeurIPS 2019), Canada: Neural Information Processing Systems Foundation.
He, F., Liu, T., Tao, D. (2019). Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. 33th Conference on Neural Information Processing Systems (NeurIPS 2019), Canada: Neural Information Processing Systems Foundation.
Yang, E., Liu, T., Deng, C., Liu, W., Tao, D. (2019). DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Tian, X., Li, Y., Liu, T., Wang, X., Tao, D. (2019). Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space. IEEE Transactions on Neural Networks and Learning Systems, 30(6), 1818-1830. [More Information]
Wang, Q., Yu, J., Liu, T., Liu, W. (2019). Guest editorial: Visual domain adaptation and generalisation. IET Computer Vision, 13(2), 87-89. [More Information]
Gong, C., Liu, T., Yang, J., Tao, D. (2019). Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3471-3483. [More Information]
Zhang, C., Ren, D., Liu, T., Yang, J., Gong, C. (2019). Positive and Unlabeled Learning with Label Disambiguation. 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macao: International Joint Conferences on Artificial Intelligence. [More Information]
Xu, Y., Gong, M., Liu, T., Batmanghelich, K., Wang, C. (2019). Robust Angular Local Descriptor Learning. 14th Asian Conference on Computer Vision (ACCV 2018), Cham: Springer. [More Information]
Lou, Y., Duan, L., Luo, Y., Chen, Z., Liu, T., Wang, S., Gao, W. (2019). Towards digital retina in smart cities: A model generation, utilization and communication paradigm. 2019 IEEE International Conference on Multimedia and Expo (ICME 2019), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Luo, Y., Wen, Y., Liu, T., Tao, D. (2019). Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4), 1013-1026. [More Information]
Guan, N., Liu, T., Zhang, Y., Tao, D., Davis, L. (2019). Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1), 246-259. [More Information]
Deng, C., Yang, E., Liu, T., Li, J., Liu, W., Tao, D. (2019). Unsupervised Semantic-Preserving Adversarial Hashing for Image Search. IEEE Transactions on Image Processing, 28(8), 4032-4044. [More Information]

show 10 more

2018

Gong, C., Liu, T., Tang, Y., Yang, J., Yang, J., Tao, D. (2018). A Regularization Approach for Instance-Based Superset Label Learning. IEEE Transactions on Cybernetics, 48(3), 967-978. [More Information]
Yu, X., Liu, T., Gong, M., Batmanghelich, K., Tao, D. (2018). An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, Utah: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Shen, X., Tian, X., Liu, T., Xu, F., Tao, D. (2018). Continuous Dropout. IEEE Transactions on Neural Networks and Learning Systems, 29(9), 3926-3937. [More Information]
Yu, B., Liu, T., Gong, M., Ding, C., Tao, D. (2018). Correcting the triplet selection bias for triplet loss. 15th European Conference on Computer Vision (ECCV2018), Cham: Springer. [More Information]
Ma, K., Fu, H., Liu, T., Wang, Z., Tao, D. (2018). Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks. IEEE Transactions on Image Processing, 27(10), 5155-5166. [More Information]
Li, Y., Tian, X., Gong, M., Liu, Y., Liu, T., Zhang, K., Tao, D. (2018). Deep domain generalization via conditional invariant adversarial networks. 15 th European Conference on Computer Vision ECCV 2018, Cham: Springer. [More Information]
Li, Y., Gong, M., Tian, X., Liu, T., Tao, D. (2018). Domain Generalization via Conditional Invariant Representations. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), Palo Alto: AAAI Press.
Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T. (2018). Fast Supervised Discrete Hashing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2), 490-496. [More Information]
Yu, X., Liu, T., Gong, M., Tao, D. (2018). Learning with Biased Complementary Labels. 15th European Conference on Computer Vision ECCV 2018, Cham: Springer. [More Information]
Wang, R., Liu, T., Tao, D. (2018). Multiclass Learning with Partially Corrupted Labels. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2568-2580. [More Information]
Li, Y., Tian, X., Liu, T., Tao, D. (2018). On better exploring and exploiting task relationships in multitask learning: Joint model and feature learning. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1975-1985. [More Information]
Luo, Y., Liu, T., Wen, Y., Tao, D. (2018). Online heterogeneous transfer metric learning. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm: International Joint Conferences on Artificial Intelligence. [More Information]
Du, Y., Liu, T., Li, Y., Duan, R., Tao, D. (2018). Quantum divide-and-conquer anchoring for separable non-negative matrix factorization. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm: International Joint Conferences on Artificial Intelligence. [More Information]
Yang, E., Deng, C., Liu, T., Liu, W., Tao, D. (2018). Semantic structure-based unsupervised deep hashing. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm: International Joint Conferences on Artificial Intelligence. [More Information]
Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T. (2018). Supervised Discrete Hashing with Relaxation. IEEE Transactions on Neural Networks and Learning Systems, 29(3), 608-617. [More Information]

show 12 more

2017

Liu, T., Tao, D., Song, M., Maybank, S. (2017). Algorithm-Dependent Generalization Bounds for Multi-Task Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(2), 227-241. [More Information]
Liu, T., Lugosi, G., Neu, G., Tao, D. (2017). Algorithmic stability and hypothesis complexity. The 34th International Conference on Machine Learning, (ICML 2017), online: Proceedings of Machine Learning Research.
Ma, K., Liu, W., Liu, T., Wang, Z., Tao, D. (2017). dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs. IEEE Transactions on Image Processing, 26(8), 3951-3964. [More Information]
Liu, Q., Sun, Y., Wang, C., Liu, T., Tao, D. (2017). Elastic net hypergraph learning for image clustering and semi-supervised classification. IEEE Transactions on Image Processing, 26(1), 452-463. [More Information]
Luo, Y., Wen, Y., Liu, T., Tao, D. (2017). General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer. 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne: International Joint Conferences on Artificial Intelligence. [More Information]
Zhang, Y., Du, B., Zhang, L., Liu, T. (2017). Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 894-906. [More Information]
Liu, T., Gong, M., Tao, D. (2017). Large-Cone Nonnegative Matrix Factorization. IEEE Transactions on Neural Networks and Learning Systems, 28(9), 2129-2142. [More Information]
Yu, X., Liu, T., Wang, X., Tao, D. (2017). On Compressing Deep Models by Low Rank and Sparse Decomposition. 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Liu, H., Wu, J., Liu, T., Tao, D., Fu, Y. (2017). Spectral Ensemble Clustering via Weighted K-means: Theoretical and Practical Evidence. IEEE Transactions On Knowledge And Data Engineering, 29(5), 1129-1143. [More Information]
Liu, T., Yang, Q., Tao, D. (2017). Understanding How Feature Structure Transfers in Transfer Learning. 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne: International Joint Conferences on Artificial Intelligence. [More Information]

show 7 more

2016

Liu, T., Tao, D. (2016). Classification with Noisy Labels by Importance Reweighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 447-461. [More Information]
Liu, T., Tao, D., Xu, D. (2016). Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes. Neural Computation, 28(10), 2213-2249. [More Information]
Xiong, H., Liu, T., Tao, D. (2016). Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair. 30th AAAI Conference on Artificial Intelligence (AAAI 2016), United States: AAAI Press.
Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., Scholkopf, B. (2016). Domain Adaptation with Conditional Transferable Components. 33rd International Conference on Machine Learning (ICML 2016), Stroudsburg: International Machine Learning Society.
Xiong, H., Liu, T., Tao, D., Shen, H. (2016). Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repairing. IEEE Transactions on Image Processing, 25(8), 3626-3637. [More Information]
Xu, C., Liu, T., Tao, D., Xu, C. (2016). Local Rademacher Complexity for Multi-Label Learning. IEEE Transactions on Image Processing, 25(3), 1495-1507. [More Information]
Liu, T., Tao, D. (2016). On the performance of Manhattan nonnegative matrix factorization. IEEE Transactions on Neural Networks and Learning Systems, 27(9), 1851-1863. [More Information]
Gui, J., Liu, T., Tao, D., Sun, Z., Tan, T. (2016). Representative Vector Machines: A Unified Framework for Classical Classifiers. IEEE Transactions on Cybernetics, 46(8), 1877-1888. [More Information]
Li, X., Liu, T., Deng, J., Tao, D. (2016). Video face editing using temporal-spatial-smooth warping. ACM Transactions on Intelligent Systems and Technology, 7(3), 1-28. [More Information]

show 6 more

2015

Gong, C., Liu, T., Tao, D., Fu, K., Tu, E., Yang, J. (2015). Deformed Graph Laplacian for Semisupervised Learning. IEEE Transactions on Neural Networks and Learning Systems, 26(10), 2261-2274. [More Information]
Li, Y., Tian, X., Liu, T., Tao, D. (2015). Multi-task model and feature joint learning. 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires: AAAI Press.
Luo, Y., Liu, T., Tao, D., Xu, C. (2015). Multiview matrix completion for multilabel image classification. IEEE Transactions on Image Processing, 24(8), 2355-2368. [More Information]
Lu, Y., Xie, F., Liu, T., Jiang, Z., Tao, D. (2015). No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model. IEEE Signal Processing Letters, 22(10), 1811-1815. [More Information]
Liu, H., Liu, T., Wu, J., Tao, D., Fu, Y. (2015). Spectral ensemble clustering. The 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), New York: Association for Computing Machinery (ACM). [More Information]

show 2 more

2014

Luo, Y., Liu, T., Tao, D., Xu, C. (2014). Decomposition-based transfer distance metric learning for image classification. IEEE Transactions on Image Processing, 23(9), 3789-3801. [More Information]
Shao, M., Li, S., Liu, T., Tao, D., Huang, T., Fu, Y. (2014). Learning relative features through adaptive pooling for image classification. 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW 2014), Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Liu, T., Tao, D. (2014). On the robustness and generalization of Cauchy regression. 2014 4th IEEE International Conference on Information Science and Technology (ICIST 2014), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

Selected Grants

2024

Visual methods for advanced automation of underwater manipulation, Ila V, Liu T, Williams S, Australian Research Council (ARC)/Linkage Projects (LP)
Self-supervised feature learning for rapid processing of marine imagery, Williams S, Liu T, Marzinelli E, Australian Research Council (ARC)/Linkage Projects (LP)

2023

Modelling Adversarial Noise for Trustworthy Data Analytics, Liu T, Australian Research Council (ARC)/Future Fellowships (FT)
AI-enhanced Online Shopping, Bao W, Ge L, Liu T, Yuan D, University of Sydney/Industry Collaboration Program

2022

Transfer Learning Handling Causally Bilateral Shift, Liu T, Xu D, Australian Research Council (ARC)/Discovery Projects (DP)

2021

Differential privacy preservation machine learning on image data, Liu T, Commonwealth Scientific and Industrial Research Organisation/Research Grant
Using Advanced IoT-based Machine Learning for In Home Quality Ageing, Bouguettaya A, Liu T, Liu N, Low L, NSW Smart Sensing Network/NSSN Grand Challenge Grant

2020

Trustworthy learning for advancing cardiovascular disease research at cellular resolutions, Lin Y, Liu T, Cardiovascular Initiative/Catalyst Awards

2019

Feature-Dependent Label Noise Learning for Big Data Analytics, Liu T, Australian Research Council (ARC)/Discovery Early Career Researcher Award (DECRA)

2018

Streaming Label Learning for Leaching Knowledge from Labels on the Fly, Tao D, Xu C, Liu T, Lugosi G, Australian Research Council (ARC)/Discovery Projects (DP)

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