Feiyang Ye

23 papers A* 6A 1B 1Journal 15
YearRankTypeTitle / Venue / Authors
2026 J jnl
Dual-balancing for multi-task learning.
Neural Networks
Baijiong Lin, Weisen Jiang, Feiyang Ye, Yu Zhang, Pengguang Chen, Ying-Cong Chen, Shu Liu, Ivor W. Tsang, James T. Kwok
2026 J jnl
HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models.
CoRR
Xin Yan, Zhenglin Wan, Feiyang Ye, Xingrui Yu, Hangyu Du, Yang You, Ivor W. Tsang
2026 J jnl
Know Your Step: Faster and Better Alignment for Flow Matching Models via Step-aware Advantages.
CoRR
Zhixiong Yue, Zixuan Ni, Feiyang Ye, Jinshan Zhang, Sheng Shen, Zhenpeng Mi
2026 J jnl
One-Token Verification for Reasoning Correctness Estimation.
CoRR
Zhan Zhuang, Xiequn Wang, Zebin Chen, Feiyang Ye, Ying Wei, Kede Ma, Yu Zhang
2025 A* conf
MTSAM: Multi-Task Fine-Tuning for Segment Anything Model.
ICLR
Xuehao Wang, Zhan Zhuang, Feiyang Ye, Yu Zhang
2025 A* conf
Sharpness-Aware Black-Box Optimization.
ICLR
Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor W. Tsang
2024 A conf
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization.
ECAI
Feiyang Ye, Baijiong Lin, Xiaofeng Cao, Yu Zhang, Ivor W. Tsang
2024 J jnl
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization.
CoRR
Feiyang Ye, Baijiong Lin, Xiaofeng Cao, Yu Zhang, Ivor W. Tsang
2024 J jnl
A Versatile Framework for Unsupervised Domain Adaptation Based on Instance Weighting.
IEEE Trans. Image Process.
Jinjing Zhu, Feiyang Ye, Qiao Xiao, Pengxin Guo, Yu Zhang, Qiang Yang
2024 A* conf
Adaptive Stochastic Gradient Algorithm for Black-box Multi-Objective Learning.
ICLR
Feiyang Ye, Yueming Lyu, Xuehao Wang, Yu Zhang, Ivor W. Tsang
2024 J jnl
Multi-objective meta-learning.
Artif. Intell.
Feiyang Ye, Baijiong Lin, Zhixiong Yue, Yu Zhang, Ivor W. Tsang
2024 A* conf
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting.
NeurIPS
Jinliang Deng, Feiyang Ye, Du Yin, Xuan Song, Ivor W. Tsang, Hui Xiong
2024 J jnl
Sharpness-Aware Black-Box Optimization.
CoRR
Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor W. Tsang
2024 J jnl
Task-Aware Low-Rank Adaptation of Segment Anything Model.
CoRR
Xuehao Wang, Feiyang Ye, Yu Zhang
2023 J jnl
A Scale-Invariant Task Balancing Approach for Multi-Task Learning.
CoRR
Baijiong Lin, Weisen Jiang, Feiyang Ye, Yu Zhang, Pengguang Chen, Ying-Cong Chen, Shu Liu
2023 J jnl
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting.
CoRR
Jinjing Zhu, Feiyang Ye, Qiao Xiao, Pengxin Guo, Yu Zhang, Qiang Yang
2023 A* conf
Multi-Task Learning via Time-Aware Neural ODE.
IJCAI
Feiyang Ye, Xuehao Wang, Yu Zhang, Ivor W. Tsang
2023 B conf
Partially-Labeled Domain Generalization via Multi-Dimensional Domain Adaptation.
IJCNN
Feiyang Ye, Jianghan Bao, Yu Zhang
2022 J jnl
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning.
Trans. Mach. Learn. Res.
Baijiong Lin, Feiyang Ye, Yu Zhang, Ivor W. Tsang
2021 J jnl
A Closer Look at Loss Weighting in Multi-Task Learning.
CoRR
Baijiong Lin, Feiyang Ye, Yu Zhang
2021 A* conf
Multi-Objective Meta Learning.
NeurIPS
Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, Yu Zhang
2021 J jnl
Multi-Objective Meta Learning.
CoRR
Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, Yu Zhang
2021 J jnl
Safe Multi-Task Learning.
CoRR
Pengxin Guo, Feiyang Ye, Yu Zhang