B. Peng, Z. Fang^✉, G. Zhang, J. Lu.
Knowledge Distillation with Auxiliary Variable.
In International Conference on Machine Learning (ICML 2024), Published Online, 2024 (CORE A*).
[ Openreview ] [ CODE]

B. Peng, Y. Luo, Y. Zhang, Y. Li, Z. Fang^✉.
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection.
In International Conference on Learning Representations (ICLR 2024), Published Online, 2024 (CORE A*).
[ Openreview ] [ CODE]

X. Du^†, Z. Fang^†, Ilias Diakonikolas, Y. Li.
How Does Wild Data Provably Help OOD Detection?
In International Conference on Learning Representations (ICLR 2024), Published Online, 2024 (CORE A*).
[ Openreview ] [ CODE]

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), Published Online, 2023 (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), Published Online, 2023 (CORE A*).
[ Github ] [ CODE][ Spotlight ]

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

L. Zhong^†, Z. Fang^†, F. Liu^†, B. Yuan, G. Zhan, 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, 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 ]

J. Nie, Y. Luo, S. Ye, Y. Zhang, X. Tian, Z. Fang.
Out-of-Distribution Detection with Virtual Outlier Smoothing.
International Journal of Computer Vision, Published Online, 2024 (ERA&CORE A*).
[ Link ]

Z. Fang, Y. Li, F. Liu, B. Han, J. Lu.
On the Learnability of Out-of-distribution Detection.
Journal of Machine Learning Research, Published Online, 2024 (ERA&CORE A*).
[ Link ]

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, 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 ]

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.