Feiyang Ye

According to our database, Feiyang Ye authored at least 16 papers between 2021 and 2025.

Collaborative distances:

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

2025

MTSAM: Multi-Task Fine-Tuning for Segment Anything Model.

Xuehao Wang, Zhan Zhuang, Feiyang Ye, Yu Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Sharpness-Aware Black-Box Optimization.

Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor W. Tsang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

A Versatile Framework for Unsupervised Domain Adaptation Based on Instance Weighting.

Jinjing Zhu, Feiyang Ye, Qiao Xiao, Pengxin Guo, Yu Zhang, Qiang Yang

IEEE Trans. Image Process., 2024

Task-Aware Low-Rank Adaptation of Segment Anything Model.

Xuehao Wang, Feiyang Ye, Yu Zhang

CoRR, 2024

Multi-objective meta-learning.

Feiyang Ye, Baijiong Lin, Zhixiong Yue, Yu Zhang, Ivor W. Tsang

Artif. Intell., 2024

Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting.

Jinliang Deng, Feiyang Ye, Du Yin, Xuan Song, Ivor W. Tsang, Hui Xiong

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Adaptive Stochastic Gradient Algorithm for Black-box Multi-Objective Learning.

Feiyang Ye, Yueming Lyu, Xuehao Wang, Yu Zhang, Ivor W. Tsang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization.

Feiyang Ye, Baijiong Lin, Xiaofeng Cao, Yu Zhang, Ivor W. Tsang

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023

A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting.

Jinjing Zhu, Feiyang Ye, Qiao Xiao, Pengxin Guo, Yu Zhang, Qiang Yang

CoRR, 2023

A Scale-Invariant Task Balancing Approach for Multi-Task Learning.

Baijiong Lin, Weisen Jiang, Feiyang Ye, Yu Zhang, Pengguang Chen, Ying-Cong Chen, Shu Liu

CoRR, 2023

Partially-Labeled Domain Generalization via Multi-Dimensional Domain Adaptation.

Feiyang Ye, Jianghan Bao, Yu Zhang

Proceedings of the International Joint Conference on Neural Networks, 2023

Multi-Task Learning via Time-Aware Neural ODE.

Feiyang Ye, Xuehao Wang, Yu Zhang, Ivor W. Tsang

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2022

Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning.

Baijiong Lin, Feiyang Ye, Yu Zhang, Ivor W. Tsang

Trans. Mach. Learn. Res., 2022

2021

A Closer Look at Loss Weighting in Multi-Task Learning.

Baijiong Lin, Feiyang Ye, Yu Zhang

CoRR, 2021

Safe Multi-Task Learning.

Pengxin Guo, Feiyang Ye, Yu Zhang

CoRR, 2021

Multi-Objective Meta Learning.

Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, Yu Zhang

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