56 citations · 110 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
A Tutorial on Bayesian Optimization
Peter I. Frazier
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous doma…
Discretization-free Knowledge Gradient Methods for Bayesian Optimization
Jian Wu, Peter I. Frazier
This paper studies Bayesian ranking and selection (R&S) problems with correlated prior beliefs and continuous domains, i.e. Bayesian optimization (BO). Knowledge gradient methods […
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning
Stephen N. Pallone, Peter I. Frazier, Shane G. Henderson
We analyze the problem of learning a single user's preferences in an active learning setting, sequentially and adaptively querying the user over a finite time horizon. Learning is…