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ORCIDFeng Liu 0003
Feng (Alex) Liu – 刘峰
Person information
- unicode name: 刘峰
- affiliation: University of Melbourne, School of Mathematics and Statistics, Australia
- affiliation (former): University of Technology Sydney, Australian Artificial Intelligence Institute, NSW, Australia
- affiliation (2015-2016): Dongbei University of Finance and Economics, School of Statistics, Dalian, China
Other persons with the same name
- Feng Liu — disambiguation page
- Feng Liu 0001
![[0000-0002-7828-2090]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-7828-2090")
— Chinese Academy of Sciences, State Key Laboratory of Information Security, Institute of Software, Beijing, China - Feng Liu 0002 — Nanjing University of Science and Technology, School of Electronic and Optical Engineering, China
- Feng Liu 0004 — Shanghai Maritime University, College of Information Engineering, China
- Feng Liu 0005 — The University of Queensland, School of Information Technology and Electrical Engineering, Brisbane, QLD, Australia
- Feng Liu 0006 — Nanjing University, Department of Physics, China
- Feng Liu 0007 — Chinese Academy of Sciences, State Key Laboratory of Membrane Biology, Institute of Zoology, Beijing, China
- Feng Liu 0008 — Hefei University of Technology, School of Economics / Research Center of Industry Information, China
- Feng Liu 0009 — Beihang University, Department of School of Astronautics, Beijing, China (and 1 more)
- Feng Liu 0010 — Beihang University, School of Electronics and Information Engineering, Beijing, China (and 1 more)
- Feng Liu 0011 — Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA (and 2 more)
- Feng Liu 0012 — Nankai University, College of Electronic Information and Optical Engineering, Tianjin, China (and 1 more)
- Feng Liu 0013 — Shenzhen University, School of Computer Science and Software Engineering, China (and 2 more)
- Feng Liu 0014 — Tsinghua University, Department of Electrical Engineering, State Key Laboratory of Power Systems, Beijing, China
- Feng Liu 0015 — Portland State University, Computer Science Department, OR, USA (and 2 more)
- Feng Liu 0016 — Avaya Labs Research, Basking Ridge, NJ, USA
- Feng Liu 0017 — Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, China
- Feng Liu 0018 — Nanjing University of Finance and Economics, School of Information Engineering, China
- Feng Liu 0019 — Southwest University, Faculty of Materials and Energy, Institute for Clean Energy and Advanced Materials, Chongqing, China
- Feng Liu 0020 — Dongbei University of Finance and Economics, School of Management Science and Engineering, Dalian, China (and 1 more)
- Feng Liu 0021 — Ludwig Maximilians University Munich, Germany
- Feng Liu 0022 — Northwestern Polytechnical University, School of Electronics and Information, Xi'an, China
- Feng Liu 0023 — Beihang University, School of Instrumentation and Optoelectronic Engineering, Beijing, China
- Feng Liu 0024 — Anhui University, School of Computer Science and Technology, Hefei, China
- Feng Liu 0025 — Xidian University, Key Laboratory of Antennas and Microwave Technologies, Xi'an, China
- Feng Liu 0026 — Harbin Engineering University, College of Shipbuilding Engineering, Harbin, China
- Feng Liu 0027 — Beijing Jiaotong University, School of Electronic and Information Engineering, Beijing, China
- Feng Liu 0028 — Nanjing University of Posts and Telecommunications, Jiangsu Provincial Key Lab of Image Processing and Image Communication, Nanjing, China
- Feng Liu 0029 — National University of Defense Technology, School of Computer Science, National Lab of Parallel Distributed Processing, Changsha, China
- Feng Liu 0030 — Chinese Academy of Sciences, Research Center on Fictitious Economy and Data Science, Beijing, China (and 2 more)
- Feng Liu 0031 — Vrije Universiteit Brussel, Computational Modeling Lab, Brussels, Belgium
- Feng Liu 0032 — National University of Defense Technology, College of Electronic Engineering, Hefei, China
- Feng Liu 0033 — Columbia University Medical Center, Department of Psychiatry, New York, NY, USA (and 1 more)
- Feng Liu 0034 — Harbin Institute of Technology, Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen, China
- Feng Liu 0035 — Tianjin Medical University, Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin, China (and 1 more)
- Feng Liu 0036 — Deepwise AI Lab, Beijing, China (and 2 more)
- Feng Liu 0037 — Michigan State University, Department of Computer Science and Engineering, Computer Vision Lab, East Lansing, MI, USA (and 1 more)
- Feng Liu 0038 — Nanjing Tech University, College of Electrical Engineering and Control Science, China (and 2 more)
- Feng Liu 0039 — East China Normal University, China
- Feng Liu 0040 — Huawei Munich Research Center, Germany
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2020 – today
- 2024
[j15]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms. IEEE Trans. Cybern. 54(2): 1048-1061 (2024)- 2023
- [j14]Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang:
Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 1087-1105 (2023) - [j13]Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han:
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. Trans. Mach. Learn. Res. 2023 (2023) - [j12]Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu:
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 34(8): 3859-3873 (2023) - [c32]Xunye Tian, Feng Liu:
Take a Close Look at the Optimization of Deep Kernels for Non-parametric Two-Sample Tests. ADC 2023: 17-29 - [c31]Yiliao Song, Tingru Cui, Feng Liu:
Designing Fair AI Systems: How Explanation specificity Influences Users’ Perceived Fairness and Trusting Intentions. ECIS 2023 - [c30]Ke Liu, Feng Liu, Haishuai Wang, Ning Ma, Jiajun Bu, Bo Han:
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis. ICCV 2023: 5451-5460 - [c29]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. ICLR 2023 - [c28]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML 2023: 8260-8275 - [c27]Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han:
Detecting Out-of-distribution Data through In-distribution Class Prior. ICML 2023: 15067-15088 - [c26]Shuhai Zhang, Feng Liu, Jiahao Yang, Yifan Yang, Changsheng Li, Bo Han, Mingkui Tan:
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score. ICML 2023: 41429-41451 - [c25]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-distribution Detection. NeurIPS 2023 - [c24]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. NeurIPS 2023 - [c23]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. NeurIPS 2023 - [c22]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. NeurIPS 2023 - [c21]Tingru Cui, Sharon Li, Kaiping Chen, James Bailey, Feng Liu:
Designing Fair AI Systems: Exploring the Interaction of Explainable AI and Task Objectivity on Users' Fairness Perception. PACIS 2023: 161 - [i26]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. CoRR abs/2302.03857 (2023) - [i25]Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han:
Out-of-distribution Detection with Implicit Outlier Transformation. CoRR abs/2303.05033 (2023) - [i24]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. CoRR abs/2305.00374 (2023) - [i23]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-Distribution Detection. CoRR abs/2311.01796 (2023) - [i22]Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han:
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. CoRR abs/2311.03236 (2023) - 2022
- [j11]Fan Dong, Jie Lu, Yiliao Song, Feng Liu, Guangquan Zhang:
A Drift Region-Based Data Sample Filtering Method. IEEE Trans. Cybern. 52(9): 9377-9390 (2022) - [j10]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning From a Complementary-Label Source Domain: Theory and Algorithms. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7667-7681 (2022) - [c20]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c19]Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng:
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. ICML 2022: 7144-7163 - [c18]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. ICML 2022: 24743-24769 - [c17]Xiong Peng, Feng Liu, Jingfeng Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. KDD 2022: 1358-1367 - [c16]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? NeurIPS 2022 - [c15]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. NeurIPS 2022 - [i21]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms. CoRR abs/2206.04311 (2022) - [i20]Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han:
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks. CoRR abs/2206.05483 (2022) - [i19]Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng:
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack. CoRR abs/2206.07314 (2022) - [i18]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? CoRR abs/2210.14707 (2022) - [i17]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. CoRR abs/2210.15198 (2022) - 2021
- [j9]Feng Liu, Guangquan Zhang, Jie Lu:
Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks. IEEE Trans. Fuzzy Syst. 29(11): 3308-3322 (2021) - [j8]Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang:
Open Set Domain Adaptation: Theoretical Bound and Algorithm. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4309-4322 (2021) - [c14]Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang:
How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? AAAI 2021: 11079-11087 - [c13]Guangzhi Ma, Feng Liu, Guangquan Zhang, Jie Lu:
Learning from Imprecise Observations: An Estimation Error Bound based on Fuzzy Random Variables. FUZZ-IEEE 2021: 1-8 - [c12]Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. ICML 2021: 3122-3132 - [c11]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML 2021: 3564-3575 - [c10]Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland:
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. NeurIPS 2021: 5848-5860 - [c9]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. NeurIPS 2021: 20970-20982 - [c8]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [i16]Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang:
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? CoRR abs/2101.01104 (2021) - [i15]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. CoRR abs/2102.04002 (2021) - [i14]Chenhong Zhou, Feng Liu, Chen Gong, Tongliang Liu, Bo Han, William Kwok-Wai Cheung:
KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation. CoRR abs/2106.06237 (2021) - [i13]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. CoRR abs/2106.06326 (2021) - [i12]Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland:
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. CoRR abs/2106.07636 (2021) - [i11]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR abs/2106.07904 (2021) - [i10]Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng:
Local Reweighting for Adversarial Training. CoRR abs/2106.15776 (2021) - [i9]Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. CoRR abs/2106.15792 (2021) - 2020
- [b1]Feng Liu:
Towards Realistic Transfer Learning Methods: Theory and Algorithms. University of Technology Sydney, Australia, 2020 - [j7]Feng Liu, Guangquan Zhang, Jie Lu:
Heterogeneous Domain Adaptation: An Unsupervised Approach. IEEE Trans. Neural Networks Learn. Syst. 31(12): 5588-5602 (2020) - [c7]Feng Liu, Guangquan Zhang, Jie Lu:
A Novel Non-parametric Two-Sample Test on Imprecise Observations. FUZZ-IEEE 2020: 1-6 - [c6]Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML 2020: 6316-6326 - [c5]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. IJCAI 2020: 2526-2532 - [i8]Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR abs/2002.09116 (2020) - [i7]Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu:
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. CoRR abs/2006.13022 (2020) - [i6]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. CoRR abs/2007.14612 (2020) - [i5]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning from a Complementary-label Source Domain: Theory and Algorithms. CoRR abs/2008.01454 (2020) - [i4]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy is Aware of Adversarial Attacks. CoRR abs/2010.11415 (2020)
2010 – 2019
- 2019
- [j6]Hua Zuo, Jie Lu, Guangquan Zhang, Feng Liu:
Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning. IEEE Trans. Fuzzy Syst. 27(2): 291-303 (2019) - [c4]Feng Liu, Guangquan Zhang, Jie Lu:
A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios. FUZZ-IEEE 2019: 1-6 - [c3]Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang:
Unsupervised Domain Adaptation with Sphere Retracting Transformation. IJCNN 2019: 1-8 - [i3]Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama:
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. CoRR abs/1905.07720 (2019) - [i2]Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang:
Open Set Domain Adaptation: Theoretical Bound and Algorithm. CoRR abs/1907.08375 (2019) - 2018
- [j5]Yi Zhang, Jie Lu, Feng Liu, Qian Liu, Alan L. Porter, Hongshu Chen, Guangquan Zhang:
Does deep learning help topic extraction? A kernel k-means clustering method with word embedding. J. Informetrics 12(4): 1099-1117 (2018) - [j4]Anjin Liu, Jie Lu, Feng Liu, Guangquan Zhang:
Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recognit. 76: 256-272 (2018) - [j3]Feng Liu, Jie Lu, Guangquan Zhang:
Unsupervised Heterogeneous Domain Adaptation via Shared Fuzzy Equivalence Relations. IEEE Trans. Fuzzy Syst. 26(6): 3555-3568 (2018) - [c2]Feng Liu, Guangquan Zhang, Jie Lu:
Unconstrained fuzzy feature fusion for heterogeneous unsupervised domain adaptation. FUZZ-IEEE 2018: 1-8 - 2017
- [j2]Qian Zhang, Dianshuang Wu, Jie Lu, Feng Liu, Guangquan Zhang:
A cross-domain recommender system with consistent information transfer. Decis. Support Syst. 104: 49-63 (2017) - [c1]Feng Liu, Guangquan Zhang, Jie Lu:
Heterogeneous unsupervised domain adaptation based on fuzzy feature fusion. FUZZ-IEEE 2017: 1-6 - [i1]Feng Liu, Guangquan Zhang, Haiyan Lu, Jie Lu:
Heterogeneous Unsupervised Cross-domain Transfer Learning. CoRR abs/1701.02511 (2017) - 2016
- [j1]Jianzhou Wang, Feng Liu, Yiliao Song, Jing Zhao:
A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Appl. Soft Comput. 48: 281-297 (2016)
Coauthor Index
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