Yehong Zhang

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 25

Top Topics

Gaussian Process

Top Venues

Publications

Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu
COPR: Continual Human Preference Learning via Optimal Policy Regularization. CoRR (2024)
Jinglong Luo, Yehong Zhang, Zhuo Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC. ACL (Findings) (2024)
Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu
SecFormer: Towards Fast and Accurate Privacy-Preserving Inference for Large Language Models. CoRR (2024)
Shiyu Liu, Jinglong Luo, Yehong Zhang, Hui Wang, Yue Yu, Zenglin Xu
Efficient privacy-preserving Gaussian process via secure multi-party computation. J. Syst. Archit. 151 (2024)
Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet
Incentives in Private Collaborative Machine Learning. CoRR (2024)
Junlin Wei, Pengfei Lin, Jinrong Jiang, Hailong Liu, Lian Zhao, Yehong Zhang, Xiang Han, Feng Zhang, Jian Huang, Yuzhu Wang, Youyun Li, Yue Yu, Xuebin Chi
Accelerating LASG/IAP climate system ocean model version 3 for performance portability using Kokkos. Future Gener. Comput. Syst. 160 (2024)
Xin Mu, Yu Wang, Zhengan Huang, Junzuo Lai, Yehong Zhang, Hui Wang, Yue Yu
EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection. AAAI (2024)
Rachael Hwee Ling Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet
Incentives in Private Collaborative Machine Learning. NeurIPS (2023)
Mohit Rajpal, Lac Gia Tran, Yehong Zhang, Bryan Kian Hsiang Low
Hessian-Aware Bayesian Optimization for Decision Making Systems. CoRR (2023)
Xin Mu, Yu Wang, Zhengan Huang, Junzuo Lai, Yehong Zhang, Hui Wang, Yue Yu
EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection. CoRR (2023)
Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, Zenglin Xu
Practical privacy-preserving Gaussian process regression via secret sharing. UAI (2023)
Yehong Zhang, Jun Wu, Hui Xu
Fuzz Driver Synthesis for Rust Generic APIs. CoRR (2023)
Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, Zenglin Xu
Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing. CoRR (2023)
Xin Mu, Yu Wang, Yehong Zhang, Jiaqi Zhang, Hui Wang, Yang Xiang, Yue Yu
Model Provenance via Model DNA. CoRR (2023)
Mohit Rajpal, Yehong Zhang, Bryan Kian Hsiang Low
Pruning during training by network efficacy modeling. Mach. Learn. 112 (7) (2023)
Rachael Hwee Ling Sim, Yehong Zhang, Bryan Kian Hsiang Low, Patrick Jaillet
Collaborative Bayesian Optimization with Fair Regret. ICML (2021)
Wei Zeng, Xiaozhe Ren, Teng Su, Hui Wang, Yi Liao, Zhiwei Wang, Xin Jiang, ZhenZhang Yang, Kaisheng Wang, Xiaoda Zhang, Chen Li, Ziyan Gong, Yifan Yao, Xinjing Huang, Jun Wang, Jianfeng Yu, Qi Guo, Yue Yu, Yan Zhang, Jin Wang, Hengtao Tao, Dasen Yan, Zexuan Yi, Fang Peng, Fangqing Jiang, Han Zhang, Lingfeng Deng, Yehong Zhang, Zhe Lin, Chao Zhang, Shaojie Zhang, Mingyue Guo, Shanzhi Gu, Gaojun Fan, Yaowei Wang, Xuefeng Jin, Qun Liu, Yonghong Tian
PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation. CoRR (2021)
Tong Teng, Jie Chen, Yehong Zhang, Bryan Kian Hsiang Low
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression. AAAI (2020)
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low
Collaborative Machine Learning with Incentive-Aware Model Rewards. CoRR (2020)
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low
Collaborative Machine Learning with Incentive-Aware Model Rewards. ICML (2020)
Yehong Zhang, Zhongxiang Dai, Kian Hsiang Low
Bayesian Optimization with Binary Auxiliary Information. CoRR (2019)
Tong Teng, Jie Chen, Yehong Zhang, Kian Hsiang Low
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression. CoRR (2019)
Yehong Zhang, Zhongxiang Dai, Bryan Kian Hsiang Low
Bayesian Optimization with Binary Auxiliary Information. UAI (2019)
Yuhui Wang, Christian von der Weth, Yehong Zhang, Kian Hsiang Low, Vivek K. Singh, Mohan S. Kankanhalli
Concept Based Hybrid Fusion of Multimodal Event Signals. ISM (2016)
Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
Near-Optimal Active Learning of Multi-Output Gaussian Processes. AAAI (2016)
Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan S. Kankanhalli
Near-Optimal Active Learning of Multi-Output Gaussian Processes. CoRR (2015)