7 citations · 21 across the 4 of their papers we have counts for
5 papers
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
Zhiyuan Jerry Lin, Raul Astudillo, Peter I. Frazier +1
We consider Bayesian optimization of expensive-to-evaluate experiments that generate vector-valued outcomes over which a decision-maker (DM) has preferences. These preferences are…
Thinking inside the box: A tutorial on grey-box Bayesian optimization
Raul Astudillo, Peter I. Frazier
Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate objective functions. Classical BO methods assume that the objective function is a black b…
Bayesian Optimization of Risk Measures
Sait Cakmak, Raul Astudillo, Peter Frazier +1
We consider Bayesian optimization of objective functions of the form , where is a black-box expensive-to-evaluate function and denotes either the VaR or CVaR…
Multi-Attribute Bayesian Optimization With Interactive Preference Learning
Raul Astudillo, Peter I. Frazier
We consider black-box global optimization of time-consuming-to-evaluate functions on behalf of a decision-maker (DM) whose preferences must be learned. Each feasible design is asso…
Bayesian Optimization of Composite Functions
Raul Astudillo, Peter I. Frazier
We consider optimization of composite objective functions, i.e., of the form , where is a black-box derivative-free expensive-to-evaluate function with vector-val…