2 citations · 2 across the 6 of their papers we have counts for
7 papers
Direct Regret Optimization in Bayesian Optimization
Fengxue Zhang, Yuxin Chen
Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate hand-crafted acquisition functions…
A Machine Learning Approach Capturing Hidden Parameters in Autonomous Thin-Film Deposition
Yuanlong Zheng, Connor Blake, Layla Mravac +3
The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully auto…
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation
Diantong Li, Fengxue Zhang, Chong Liu +1
Multi-objective Bayesian optimization has been widely adopted in scientific experiment design, including drug discovery and hyperparameter optimization. In practice, regulatory or…
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes
Minbiao Han, Fengxue Zhang, Yuxin Chen
This paper investigates the challenge of learning in black-box games, where the underlying utility function is unknown to any of the agents. While there is an extensive body of lit…
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints
Fengxue Zhang, Zejie Zhu, Yuxin Chen
Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as scientific exper…
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Fengxue Zhang, Jialin Song, James Bowden +4
We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, whic…