Parts-dependent Label Noise: Towards Instance-dependent Label Noise. [PDF]
X. Xia, T. Liu, B. Han, N. Wang, M. Gong, H. Liu, G. Niu, D. Tao, and M. Sugiyama.

Class2Simi: A New Perspective on Learning with Label Noise. [PDF]
S. Wu, X. Xia, T. Liu, B. Han, M. Gong, N. Wang, H. Liu, and G. Niu.

Multi-Class Classification from Noisy-Similarity-Labeled Data. [PDF]
S. Wu*, X. Xia*, T. Liu, B. Han, M. Gong, N. Wang, H. Liu, and G. Niu.

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. [PDF]
Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama.

Towards mixture proportion estimation without irreducibility. [PDF]
Y. Yao, T. Liu, B. Han, M. Gong, G. Niu, M. Sugiyama, and D. Tao.

Confidence Scores Make Instance-dependent Label-noise Learning Possible. [PDF]
A. Berthon, B. Han, G. Niu, T. Liu, and M. Sugiyama.

Where is the bottleneck of adversarial learning with unlabeled data? [PDF]
J. Zhang*, B. Han*, G. Niu, T. Liu, and M. Sugiyama.

Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces.
J. Deng, J. Guo, T. Liu, M. Gong, and S Zafeiriou.
In ECCV, 2020.

Deep Heterogeneous Multi-Task Metric Learning for Visual Recognition and Retrieval.
S. Gan, Y. Luo, Y. Wen, T. Liu, and H. Hu.
In ACM MM, 2020.

Learning with Bounded Instance- and Label-dependent Label Noise.
J. Cheng, T. Liu, K. Rao, and D. Tao.
In ICML, 2020.

Label-Noise Robust Domain Adaptation.
X. Yu, T. Liu, M. Gong, K. Zhang, K. Batmanghelich, and D. Tao.
In ICML, 2020.

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks.
Y. Zhang, Y. Li, T. Liu, and X. Tian.
In ICML, 2020.

LTF: A Label Transformation Framework for Correcting Label Shift.
J. Guo, M. Gong, T. Liu, K. Zhang, and D. Tao.
In ICML, 2020.

Generative-Discriminative Complementary Learning. [PDF]
Y. Xu, M. Gong, J. Chen, T. Liu, K. Zhang, and K. Batmanghelich.
In AAAI, 2020.

Diversified Bayesian Nonnegative Matrix Factorization. [PDF]
M. Qiao, J. Yu, T. Liu, X. Wang, and D. Tao.
In AAAI, 2020.

Are Anchor Points Really Indispensable in Label-Noise Learning? [PDF]
X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama.
In NeurIPS, 2019.

Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence. [PDF]
F. He, T. Liu, and D. Tao.
In NeurIPS, 2019.

DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs. [PDF]
E. Yang, T. Liu, C. Deng, W. Liu, and D. Tao.
In CVPR, 2019.

Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer.
Y. Furusho, T. Liu, and K. Ikeda.
In INNSBDDL, 2019.

Positive and Unlabeled Learning with Label Disambiguation. [PDF]
C. Zhang, D. Ren, T. Liu, J. Yang, and C. Gong.
In IJCAI, 2019.

Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm. [PDF] [Best Paper Award]
Y. Lou, L. Duan, Y. Luo, Z. Chen, T. Liu, S. Wang, and W. Gao.
In ICME, 2019.

An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. [PDF]
X. Yu, T. Liu, M. Gong, K. Batmanghelich, and D. Tao.
In CVPR, 2018.

Learning with Biased Complementary Labels. [PDF]
X. Yu, T. Liu, M. Gong, and D. Tao.
In ECCV, 2018.

Correcting the Triplet Selection Bias for Triplet Loss. [PDF]
B. Yu, T. Liu, M. Gong, C. Ding, and D. Tao.
In ECCV, 2018.

Deep Domain Generalization via Conditional Invariant Adversarial Networks. [PDF]
Y. Li, X. Tian, M. Gong, Y. Liu, T. Liu, K. Zhang, and D. Tao.
In ECCV, 2018.

Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization. [PDF]
Y. Du, T. Liu, Y. Li, R. Duan, and D. Tao.
In IJCAI, 2018.

Online Heterogeneous Transfer Metric Learning. [PDF]
Y. Luo, T. Liu, Y. Wen, and D. Tao.
In IJCAI, 2018.

Semantic Structure-based Unsupervised Deep Hashing. [PDF]
E. Yang, C. Deng, T. Liu, W. Liu, and D. Tao.
In IJCAI, 2018.

Domain Generalization via Conditional Invariant Representations. [PDF]
Y. Li, M. Gong, X. Tian, T. Liu, and D. Tao.
In AAAI, 2018

Robust Angular Local Descriptor Learning. [PDF]
Y. Xu, M. Gong, T. Liu, K. Batmanghelich, C. Wang.
In ACCV, 2018.

Algorithmic Stability and Hypothesis Complexity. [PDF]
T. Liu, G. Lugosi, G. Neu and D. Tao.
In ICML , 2017.

On Compressing Deep Models by Low Rank and Sparse Decomposition. [PDF]
X. Yu, T. Liu, X. Wang, and D. Tao.
In CVPR, 2017.

Understanding How Feature Structure Transfers in Transfer Learning. [PDF]
T. Liu, Q. Yang, and D. Tao.
In IJCAI, 2017.

General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer. [PDF]
Y. Luo, Y. Wen, T. Liu, and D. Tao.
In IJCAI, 2017.

Domain Adaptation with Conditional Transferable Components. [PDF]
M. Gong, K. Zhang, T. Liu, D. Tao, C. Glymour, and B. Schölkopf.
In ICML, 2106.

Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair. [PDF]
H. Xiong, T. Liu, and D. Tao.
In AAAI, 2016.

Spectral Ensemble Clustering. [PDF]
H. Liu*, T. Liu*, J. Wu, D. Tao, and Y. Fu.
In KDD, 2015.

Multi-Task Model and Feature Joint Learning. [PDF]
Y. Li, X. Tian, T. Liu*, and D. Tao.
In IJCAI, 2015.

Learning Relative Features through Adaptive Pooling for Image Classification. [PDF] [Best Paper Award candidate]
M. Shao, S. Li, T. Liu*, D. Tao, T. S. Huang, and Y. Fu.
In ICME, 2014.

On the Robustness and Generalization of Cauchy Regression. [PDF] [Best Paper Award]
T. Liu and D. Tao.
In ICIST, 2014.

Orthogonal Deep Neural Networks.
K. Jia, S. Li, Y. Wen, T. Liu, and D. Tao.
IEEE T-PAMI, accepted.

Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning.
C. Gong, H. Shi, T. Liu, C. Zhang, J. Yang, and D. Tao.
IEEE T-PAMI, accepted.

Harnessing Side Information for Classification under Label Noise.
Y. Wei, C. Gong, S. Chen, T. Liu, J. Yang, and D. Tao.
IEEE T-NNLS, Accepted.

Two-Stream Deep Hashing with Class-Specific Centers for Image Search.
C. Deng, E. Yang, T. Liu, and D. Tao.
IEEE T-NNLS, Accepted.

Why ResNet Works? Residuals Generalize.
F. He, T. Liu, and D. Tao.
IEEE T-NNLS, Accepted.

Group Feedback Capsule Network.
X. Ding, N Wang, X. Gao, J. Li, X. Wang, T. Liu.
IEEE T-IP, Accepted.

Label Propagated Nonnegative Matrix Factorization for Clustering.
L. Lan, T. Liu, X. Zhang, C. Xu, and Z. Luo.
IEEE T-KDE, Accepted.

Laplacian Welsch Regularization for Robust Semi-Supervised Learning.
J. Ke, C. Gong, T. Liu, L. Zhao, J. Yang, and D. Tao.
IEEE T-CYB, Accepted.

Towards Efficient Front-end Visual Sensing for Digital Retina: A Model-Centric Paradigm.
Y. Lou, L. Duan, Y. Luo, Z. Chen, T. Liu, S. Wang, and W. Gao.
IEEE T-MM, Accepted.

Absent Multiple Kernel Learning Algorithms. [PDF]
X. Liu, L. Wang, X. Zhu, M. Li, E. Zhu, T. Liu, L. Liu, Y. Dou, and J. Yin.
IEEE T-PAMI, 42(6): 1303-1316, 2020.

Multiple Kernel k-means with Incomplete Kernels. [PDF]
X. Liu, X. Zhu, M. Li, L. Wang, E. Zhu, T. Liu, M. Kloft, D. Shen, J. Yin, and W. Gao.
IEEE T-PAMI, 42(5): 1191-1204, 2020.

Adversarial Examples for Hamming Space Search. [PDF]
E. Yang, T. Liu, C. Deng, and D. Tao.
IEEE T-CYB, 50(4): 1473-1484, 2020.

Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain. [PDF]
Y. Luo, Y. Wen, T. Liu, and D. Tao.
IEEE T-PAMI, 41(4): 1013-1026, 2019.

Truncated Cauchy Non-negative Matrix Factorization. [PDF]
N. Guan*, T. Liu*, Y. Zhang, D. Tao, and L. S. Davis.
IEEE T-PAMI, 41(1): 246-259, 2019.

Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning. [PDF]
C. Gong, T. Liu, J. Yang, and D. Tao.
IEEE T-NNLS, 30(11): 3471-3483, 2019.

Eigenfunction-Based Multitask Learning in a Reproducing Kernel Hilbert Space. [PDF]
X. Tian, Y. Li, T. Liu, X. Wang, and D. Tao.
IEEE T-NNLS, 30(6): 1818-1830, 2019.

Adaptive Morphological Reconstruction for Seeded Image Segmentation. [PDF]
T. Lei, X. Jia, T. Liu, S. Liu, H. Meng, and A. K. Nandi.
IEEE T-IP, 28(11): 5510-5523, 2019.

Unsupervised Semantic-Preserving Adversarial Hashing for Image Search. [PDF]
C. Deng, E. Yang, T. Liu, J. Li, W. Liu, and D. Tao.
IEEE T-IP, 28(8): 4032-4044, 2019.

Fast Supervised Discrete Hashing. [PDF]
J. Gui*, T. Liu*, Z. Sun, D. Tao, and T. Tan.
IEEE T-PAMI, 40(2): 490-496, 2018.

Continuous Dropout. [PDF]
X. Shen, X. Tian, F. Xu, T. Liu, and D. Tao.
IEEE T-NNLS, 29(9): 3926-3937, 2018.

Multi-class Learning with Partially Corrupted Labels. [PDF]
R. Wang, T. Liu, and D. Tao.
IEEE T-NNLS, 29(6): 2568-2580, 2018.

On Better Exploring and Exploiting Task Relationship in Multi-Task Learning: Joint Model and Feature Learning. [PDF]
Y. Li, X. Tian, T. Liu, and D. Tao.
IEEE T-NNLS, 29(5): 1975-1985, 2018.

Supervised Discrete Hashing with Relaxation. [PDF]
J. Gui*, T. Liu*, Z. Sun, D. Tao, and T. Tan.
IEEE T-NNLS, 29(3): 608-617, 2018.

Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks. [PDF]
K. Ma, H. Fu, T. Liu, Z. Wang, and D. Tao.
IEEE T-IP, 27(10): 5155-5166, 2018.

A Regularization Approach for Instance-Based Superset Label Learning. [PDF]
C. Gong, T. Liu, Y. Tang, J. Yang, J. Yang, and D. Tao.
IEEE T-CYB, 48(3): 967-978, 2018.

Algorithm-Dependent Generalization Bounds for Multi-Task Learning. [PDF]
T. Liu, D. Tao, M. Song, and S. J. Maybank.
IEEE T-PAMI, 39(2): 227-241, 2017.

Large Cone Non-negative Matrix Factorization. [PDF]
T. Liu, M. Gong, and D. Tao.
IEEE T-NNLS, 28(9): 2129-2141, 2017.

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs. [PDF]
K. Ma, W. Liu, T. Liu, Z. Wang, and D. Tao.
IEEE T-IP, 26(8): 3951-3964, 2017.

Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification. [PDF]
Q. Liu, Y. Sun, C. Wang, T. Liu, and D. Tao.
IEEE T-IP, 26(1): 452-463, 2017.

Spectral Ensemble Clustering via Weighted K-means: Theoretical and Practical Evidence. [PDF]
H. Liu, J. Wu, T. Liu, D. Tao, and Y. Fu.
IEEE T-KDE, vol. 29, no. 5, pp. 1129-1143, 2017.

Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection. [PDF]
Y. Zhang, B. Du, L. Zhang, and T. Liu.
IEEE T-GRS, 55(2): 894-906, 2017.

Classification with Noisy Labels by Importance Reweighting. [PDF]
T. Liu and D. Tao.
IEEE T-PAMI, 38(3): 447-461, 2016.

Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes. [PDF]
T. Liu, D. Tao, and D. Xu.
NECO, 28(10): 2213-2249, 2016.

On the Performance of MahNMF Manhattan Non-negative Matrix Factorization. [PDF]
T. Liu and D. Tao.
IEEE T-NNLS, 27(9): 1851-1863, 2016.

Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repair. [PDF]
H. Xiong, T. Liu, D. Tao, and H. T. Shen.
IEEE T-IP, 25(8): 3626-3637, 2016.

Local Rademacher Complexity for Multi-Label Learning. [PDF]
C. Xu, T. Liu, D. Tao, and C. Xu.
IEEE T-IP, 25(3): 1495-1507, 2016.

Representative Vector Machines: A Unified Framework for Classical Classifiers. [PDF]
J. Gui*, T. Liu*, D. Tao, Z. Sun, and T. Tan.
IEEE T-CYB, 46(8): 1877-1888, 2016.

Video Face Editing Using Temporal-Spatial-Smooth Warping. [PDF]
X. Li, T. Liu*, J. Deng, and D. Tao.
ACM T-IST, 7(3): 32, 2016.

Deformed Graph Laplacian for Semisupervised Learning. [PDF]
C. Gong, T. Liu, D. Tao, K. Fu, E. Tu, and J. Yang.
IEEE T-NNLS, 26(10): 2261-2274, 2015.

Multiview Matrix Completion for Multilabel Image Classification. [PDF]
Y. Luo, T. Liu, D. Tao, and C. Xu.
IEEE T-IP, 24(8): 2261-2274, 2015.

No Reference Quality Assessment for Multiply-Distorted Images Based on an Improved Bag-of-Words Model. [PDF]
Y. Lu, F. Xie, T. Liu, Z. Jiang, and D. Tao.
IEEE SPL, 22(10): 1811-1815, 2015.

Decomposition-Based Transfer Distance Metric Learning for Image Classification. [PDF]
Y. Luo, T. Liu, D. Tao, and C. Xu.
IEEE T-IP, 23(9): 3789-3801, 2014.