94 citations · 148 across the 10 of their papers we have counts for
10 papers
Incentives in Private Collaborative Machine Learning
Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang +3
Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and rew…
EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection
Xin Mu, Yu Wang, Zhengan Huang +4
In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important. Within this context, machine learn…
Model Provenance via Model DNA
Xin Mu, Yu Wang, Yehong Zhang +4
Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e.g., understanding where the model comes from, how it is trained, and how it is…
Dependency Structure Search Bayesian Optimization for Decision Making Models
Mohit Rajpal, Lac Gia Tran, Yehong Zhang +1
Many approaches for optimizing decision making models rely on gradient based methods requiring informative feedback from the environment. However, in the case where such feedback i…
Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing
Jinglong Luo, Yehong Zhang, Jiaqi Zhang +4
Gaussian process regression (GPR) is a non-parametric model that has been used in many real-world applications that involve sensitive personal data (e.g., healthcare, finance, etc.…
PanGu-: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
Wei Zeng, Xiaozhe Ren, Teng Su +35
Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demon…
Collaborative Machine Learning with Incentive-Aware Model Rewards
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan +1
Collaborative machine learning (ML) is an appealing paradigm to build high-quality ML models by training on the aggregated data from many parties. However, these parties are only w…
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
Tong Teng, Jie Chen, Yehong Zhang +1
This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian process regression (SGPR) models. In contrast to existing GP kernel selection algor…
Bayesian Optimization with Binary Auxiliary Information
Yehong Zhang, Zhongxiang Dai, Kian Hsiang Low
This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting correlated auxiliary informat…
Near-Optimal Active Learning of Multi-Output Gaussian Processes
Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low +1
This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena. In…