4 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…
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…
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…
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…