Feng Liu

According to our database, Feng Liu authored at least 69 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Does Confusion Really Hurt Novel Class Discovery?

Haoang Chi, Wenjing Yang, Feng Liu, Long Lan, Tao Qin, Bo Han

Int. J. Comput. Vis., August, 2024

Domain Adaptation With Interval-Valued Observations: Theory and Algorithms.

Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang

IEEE Trans. Fuzzy Syst., May, 2024

Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms.

Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang

IEEE Trans. Cybern., February, 2024

On the Learnability of Out-of-distribution Detection.

Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu

J. Mach. Learn. Res., 2024

Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds.

Shuhai Zhang, Jiahao Yang, Hui Luo, Jie Chen, Li Wang, Feng Liu, Bo Han, Mingkui Tan

CoRR, 2024

TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On.

Zhenchen Wan, Yanwu Xu, Zhaoqing Wang, Feng Liu, Tongliang Liu, Mingming Gong

CoRR, 2024

Mind the Gap Between Prototypes and Images in Cross-domain Finetuning.

Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han

CoRR, 2024

Learning Deep Kernels for Non-Parametric Independence Testing.

Nathaniel Xu, Feng Liu, Danica J. Sutherland

CoRR, 2024

Exclusive Style Removal for Cross Domain Novel Class Discovery.

Yicheng Wang, Feng Liu, Junmin Liu, Zhen Fang, Kai Sun

CoRR, 2024

Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training.

Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Optimal Kernel Choice for Score Function-based Causal Discovery.

Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong

Proceedings of the Forty-first International Conference on Machine Learning, 2024

MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence.

Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Negative Label Guided OOD Detection with Pretrained Vision-Language Models.

Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation.

Zhong Li, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu

IEEE Trans. Neural Networks Learn. Syst., August, 2023

KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation.

Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han

Trans. Mach. Learn. Res., 2023

Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms.

Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang

IEEE Trans. Pattern Anal. Mach. Intell., 2023

Designing Fair AI Systems: Exploring the Interaction of Explainable AI and Task Objectivity on Users' Fairness Perception.

Tingru Cui, Sharon Li, Kaiping Chen, James Bailey, Feng Liu

Proceedings of the 27th Pacific Asia Conference on Information Systems, 2023

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources.

Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection.

Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization.

Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning to Augment Distributions for Out-of-distribution Detection.

Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score.

Shuhai Zhang, Feng Liu, Jiahao Yang, Yifan Yang, Changsheng Li, Bo Han, Mingkui Tan

Proceedings of the International Conference on Machine Learning, 2023

Detecting Out-of-distribution Data through In-distribution Class Prior.

Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

Proceedings of the International Conference on Machine Learning, 2023

Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation.

Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han

Proceedings of the International Conference on Machine Learning, 2023

Out-of-distribution Detection with Implicit Outlier Transformation.

Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis.

Ke Liu, Feng Liu, Haishuai Wang, Ning Ma, Jiajun Bu, Bo Han

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Designing Fair AI Systems: How Explanation specificity Influences Users’ Perceived Fairness and Trusting Intentions.

Yiliao Song, Tingru Cui, Feng Liu

Proceedings of the 31st European Conference on Information Systems, 2023

Take a Close Look at the Optimization of Deep Kernels for Non-parametric Two-Sample Tests.

Xunye Tian, Feng Liu

Proceedings of the Databases Theory and Applications, 2023

2022

Learning From a Complementary-Label Source Domain: Theory and Algorithms.

Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu

IEEE Trans. Neural Networks Learn. Syst., 2022

A Drift Region-Based Data Sample Filtering Method.

Fan Dong, Jie Lu, Yiliao Song, Feng Liu, Guangquan Zhang

IEEE Trans. Cybern., 2022

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks.

Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han

CoRR, 2022

Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms.

Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang

CoRR, 2022

Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests.

Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli

CoRR, 2022

Watermarking for Out-of-distribution Detection.

Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Is Out-of-Distribution Detection Learnable?

Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks.

Xiong Peng, Feng Liu, Jingfeng Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Adversarial Attack and Defense for Non-Parametric Two-Sample Tests.

Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli

Proceedings of the International Conference on Machine Learning, 2022

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack.

Ruize Gao, Jiongxiao Wang, Kaiwen Zhou, Feng Liu, Binghui Xie, Gang Niu, Bo Han, James Cheng

Proceedings of the International Conference on Machine Learning, 2022

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data.

Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Open Set Domain Adaptation: Theoretical Bound and Algorithm.

Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang

IEEE Trans. Neural Networks Learn. Syst., 2021

Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks.

Feng Liu, Guangquan Zhang, Jie Lu

IEEE Trans. Fuzzy Syst., 2021

Local Reweighting for Adversarial Training.

Ruize Gao, Feng Liu, Kaiwen Zhou, Gang Niu, Bo Han, James Cheng

CoRR, 2021

KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation.

Chenhong Zhou, Feng Liu, Chen Gong, Tongliang Liu, Bo Han, William Kwok-Wai Cheung

CoRR, 2021

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data.

Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Bo Han

CoRR, 2021

Probabilistic Margins for Instance Reweighting in Adversarial Training.

Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data.

Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation.

Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks.

Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Bounds for Open-Set Learning.

Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning from Imprecise Observations: An Estimation Error Bound based on Fuzzy Random Variables.

Guangzhi Ma, Feng Liu, Guangquan Zhang, Jie Lu

Proceedings of the 30th IEEE International Conference on Fuzzy Systems, 2021

How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?

Zhong Li, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Towards Realistic Transfer Learning Methods: Theory and Algorithms

Feng Liu

PhD thesis, 2020

Heterogeneous Domain Adaptation: An Unsupervised Approach.

Feng Liu, Guangquan Zhang, Jie Lu

IEEE Trans. Neural Networks Learn. Syst., 2020

Maximum Mean Discrepancy is Aware of Adversarial Attacks.

Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama

CoRR, 2020

Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation.

Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Learning Deep Kernels for Non-Parametric Two-Sample Tests.

Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland

Proceedings of the 37th International Conference on Machine Learning, 2020

A Novel Non-parametric Two-Sample Test on Imprecise Observations.

Feng Liu, Guangquan Zhang, Jie Lu

Proceedings of the 29th IEEE International Conference on Fuzzy Systems, 2020

2019

Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning.

Hua Zuo, Jie Lu, Guangquan Zhang, Feng Liu

IEEE Trans. Fuzzy Syst., 2019

Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation.

Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama

CoRR, 2019

Unsupervised Domain Adaptation with Sphere Retracting Transformation.

Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang

Proceedings of the International Joint Conference on Neural Networks, 2019

A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios.

Feng Liu, Guangquan Zhang, Jie Lu

Proceedings of the 2019 IEEE International Conference on Fuzzy Systems, 2019

2018

Unsupervised Heterogeneous Domain Adaptation via Shared Fuzzy Equivalence Relations.

Feng Liu, Jie Lu, Guangquan Zhang

IEEE Trans. Fuzzy Syst., 2018

Accumulating regional density dissimilarity for concept drift detection in data streams.

Anjin Liu, Jie Lu, Feng Liu, Guangquan Zhang

Pattern Recognit., 2018

Does deep learning help topic extraction? A kernel k-means clustering method with word embedding.

Yi Zhang, Jie Lu, Feng Liu, Qian Liu, Alan L. Porter, Hongshu Chen, Guangquan Zhang

J. Informetrics, 2018

Unconstrained fuzzy feature fusion for heterogeneous unsupervised domain adaptation.

Feng Liu, Guangquan Zhang, Jie Lu

Proceedings of the 2018 IEEE International Conference on Fuzzy Systems, 2018

2017

A cross-domain recommender system with consistent information transfer.

Qian Zhang, Dianshuang Wu, Jie Lu, Feng Liu, Guangquan Zhang

Decis. Support Syst., 2017

Heterogeneous Unsupervised Cross-domain Transfer Learning.

Feng Liu, Guangquan Zhang, Haiyan Lu, Jie Lu

CoRR, 2017

Heterogeneous unsupervised domain adaptation based on fuzzy feature fusion.

Feng Liu, Guangquan Zhang, Jie Lu

Proceedings of the 2017 IEEE International Conference on Fuzzy Systems, 2017

2016

A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system.

Jianzhou Wang, Feng Liu, Yiliao Song, Jing Zhao

Appl. Soft Comput., 2016