Tongliang Liu
Selected Publications
Preprint
Are Anchor Points Really Indispensable in Label-Noise Learning? [PDF]
Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama.
Instance-Dependent PU Learning by Bayesian Optimal Relabeling. [PDF]
Fengxiang He, Tongliang Liu, Geoff Webb, and Dacheng Tao.
Learning with Bounded Instance-and Label-dependent Label Noise. [PDF]
Jiacheng Cheng, Tongliang Liu, Kotagiri Rao, and Dacheng Tao.
Transfer Learning with Label Noise. [PDF]
Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, and Dacheng Tao.
Absent Multiple Kernel Learning Algorithms. [PDF]
Xinwang Liu, Lei Wang, Xinzhong Zhu, Miaomiao Li, En Zhu, Tongliang Liu, Li Liu, Yong Dou, and Jianping Yin.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), accepted.
Multiple Kernel k-means with Incomplete Kernels. [PDF]
Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Lei Wang, En Zhu, Tongliang Liu, Marius Kloft, Dinggagn Shen, Jianping Yin, and Wen Gao.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), accepted.
2019
Truncated Cauchy Non-negative Matrix Factorization. [PDF]
Naiyang Guan*, Tongliang Liu*, Yangmuzi Zhang, Dacheng Tao, and Larry Steven Davis.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 41(1): 246-259, 2019.
Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain. [PDF]
Yong Luo, Yonggang Wen, Tongliang Liu, and Dacheng Tao.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 41(4): 1013-1026, 2019.
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs. [PDF]
Erkun Yang, Tongliang Liu, Cheng Deng, Wei Liu, and Dacheng Tao.
In Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). To appear.
2018
Fast Supervised Discrete Hashing. [PDF]
Jie Gui*, Tongliang Liu*, Zhenan Sun, Dacheng Tao, and Tieniu Tan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 40(2): 490-496, 2018.
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption. [PDF]
Xiyu Yu, Tongliang Liu, Mingming Gong, Kayhan Batmanghelich, and Dacheng Tao.
In Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018): 4480-4489.
Learning with Biased Complementary Labels. [PDF]
Xiyu Yu, Tongliang Liu, Mingming Gong, and Dacheng Tao.
In Proceedings of the 2018 European Conference on Computer Vision (ECCV(1) 2018): 69-85.
Deep Domain Generalization via Conditional Invariant Adversarial Networks. [PDF]
Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, and Dacheng Tao.
In Proceedings of the 2018 European Conference on Computer Vision (ECCV(15) 2018): 647-663.
Correcting the Triplet Selection Bias for Triplet Loss. [PDF]
Baosheng Yu, Tongliang Liu, Mingming Gong, Changxing Ding, and Dacheng Tao.
In Proceedings of the 2018 European Conference on Computer Vision (ECCV(6) 2018): 71-86.
2017
Algorithm-Dependent Generalization Bounds for Multi-Task Learning. [PDF]
Tongliang Liu, Dacheng Tao, Mingli Song, and Stephen J. Maybank.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 39(2): 227-241, 2017.
Algorithmic Stability and Hypothesis Complexity. [PDF]
Tongliang Liu, Gábor Lugosi, Gergely Neu and Dacheng Tao.
In Proceedings of the 34th International Conference on Machine Learning (ICML 2017): 2159-2167.
On Compressing Deep Models by Low Rank and Sparse Decomposition. [PDF]
Xiyu Yu, Tongliang Liu, Xinchao Wang, and Dacheng Tao.
In Proceedings of The 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017): 7370-7379.
2016
Classification with Noisy Labels by Importance Reweighting. [PDF]
Tongliang Liu and Dacheng Tao.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 38(3): 447-461, 2016.
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes. [PDF]
Tongliang Liu, Dacheng Tao, and Dong Xu.
Neural Computation (NECO), 28(10): 2213-2249, 2016.
Domain Adaptation with Conditional Transferable Components. [PDF]
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf.
In Proceedings of the 33rd International Conference on Machine Learning (ICML 2016): 2839-2848.
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