Q. Wang^†, Z. Fang^†, Y. Zhang, F. Liu, Y. Li, B. Han.
Learning to Augment Distributions for Out-of-distribution Detection.
In Advances in Neural Information Processing Systems (NeurIPS 2023), to appear (CORE A*).
[ arXiv ] [ CODE]

M. Yang, Z. Fang, Y. Zhang, Y. Du, F. Liu, J. Ton, J. Wang, J. Wang.
Invariant Learning via Probability of Sufficient an Necessary Causes.
In Advances in Neural Information Processing Systems (NeurIPS 2023), to appear (CORE A*).
[ Github ] [ CODE][ Spotlight ]

H. Zheng, Q. Wang, Z. Fang, X. Xia, F. Liu, T. Liu, B. Han.
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources.
In Advances in Neural Information Processing Systems (NeurIPS 2023), to appear (CORE A*).
[ arXiv ] [ CODE]

Y. Luo, Z. Chen, Z. Fang, Z. Zhang, Z. Huang, M. Baktashmotlagh.
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection.
In International Conference on Computer Vision (ICCV 2023), to appear (CORE A*).
[ arXiv ] [ CODE]

X. Wu, J. Lu, Z. Fang, G. Zhang.
Meta OOD Learning For Continuously Adaptive OOD Detection.
In International Conference on Computer Vision (ICCV 2023), to appear (CORE A*).
[ arXiv ] [ CODE]

R. Dai, Y. Zhang, Z. Fang, B. Han, X. Tian.
Moderately Distributional Exploration for Domain Generalization.
In International Conference on Machine Learning (ICML 2023), Published Online, 2023 (CORE A*).
[ arXiv ] [ CODE]

X. Jiang, F. Liu, Z. Fang, H. Chen, T. Liu, F. Zheng, B. Han.
Detecting Out-of-distribution Data through In-distribution Class Prior.
In International Conference on Machine Learning (ICML 2023), Published Online, 2023 (CORE A*).
[ Link ] [ CODE]

Z. Fang, Y. Li, J. Lu, J. Dong, B. Han, F. Liu.
Is Out-of-distribution Detection Learnable?
In Advances in Neural Information Processing Systems (NeurIPS 2022), Published Online, 2022 (CORE A*).
[ arXiv ] [ Pure Theory ] [ Outstanding Paper Award ] (outstanding papers:acceptance:submissions=13:2672:10411)

Z. Fang^†, J. Lu, A. Liu^†, F. Liu, G. Zhang.
Learning Bounds for Open-Set Learning.
In International Conference on Machine Learning (ICML 2021), Published Online, 2021 (CORE A*).
[ arXiv ] [ CODE ]

J. Dong^†, Z. Fang^†, A. Liu, G. Sun and T. Liu.
Confident-Anchor-Induced Multi-Source-Free Domain Adaptation.
In Advances in Neural Information Processing Systems (NeurIPS 2021), Published Online, 2021 (CORE A*).
[ Link ] [ CODE ]

J. Dong, L. Wang, Z. Fang, G. Sun, S. Xu, and X. Wang, Q. Zhu.
Federated Class-Incremental Learning.
In Conference on Computer Vision and Pattern Recognition (CVPR 2022), Published Online, 2022 (CORE A*).
[ Link ] [ CODE ]

L. Zhong^†, Z. Fang^†, F. Liu^†, B. Yuan, G. Zhang and J. Lu.
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
In AAAI Conference on Artificial Intelligence (AAAI 2021), Published Online, 2021 (CORE A*).
[ arXiv ] [ CODE ]

Y. Zhang^†, F. Liu^†, Z. Fang^†, B. Yuan, G. Zhang and J. Lu.
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation.
In International Joint Conference on Artificial Intelligence (IJCAI 2020), Published Online (CORE A*).
[ arXiv ] [ CODE ]

Z. Fang, J. Lu, F. Liu, G. Zhang.
Semi-supervised Heterogeneous Domain Adaptation: Theory and Algorithms.
IEEE Transactions on Pattern Analysis and Machine Intelligence, Published Online, 2022 (ERA&CORE A*).
[ Link ] [ CODE ]

Z. Fang, J. Lu, F. Liu, J. Xuan and G. Zhang.
Open Set Domain Adaptation: Theoretical Bound and Algorithm.
IEEE Transactions on Neural Networks and Learning Systems, Published Online, 2020 (ERA&CORE A*).
[ arXiv ] [ CODE ]

J. Dong, Y. Cong, G. Sun, Z. Fang and Z. Ding.
Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation.
IEEE Transactions on Pattern Analysis and Machine Intelligence, Published Online, 2021 (ERA&CORE A*).

Zhen Fang.
Bridging Theory and Algorithms for Open-Set and Heterogeneous Domain Adaptations.
Doctoral Thesis, Australian Artificial Intelligence Institute, University of Technology Sydney, Australia, submitted in June 2021.

Zhen Fang.
The C2 Estimates of Prescribed Curvature Equations.
Master Thesis, Xiamen University, China, July 2017.