363 citations · 1.1k across the 50 of their papers we have counts for
17 papers · 1 filter
Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong, Jiayue Wan, Raul Astudillo +3
Bayesian optimization is a powerful framework for optimizing functions that are expensive or time-consuming to evaluate. Recent work has considered Bayesian optimization of functio…
Two-step Lookahead Bayesian Optimization with Inequality Constraints
Yunxiang Zhang, Xiangyu Zhang, Peter I. Frazier
Recent advances in computationally efficient non-myopic Bayesian optimization (BO) improve query efficiency over traditional myopic methods like expected improvement while only mod…
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 […
Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson +1
Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimi…
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…
Warm Starting Bayesian Optimization
Matthias Poloczek, Jialei Wang, Peter I. Frazier
We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related proble…
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Jian Wu, Peter I. Frazier
In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architect…
Multi-Information Source Optimization
Matthias Poloczek, Jialei Wang, Peter I. Frazier
We consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where we also have access to cheaper approximations of the objective. In general, such a…
Parallel Bayesian Global Optimization of Expensive Functions
Jialei Wang, Scott C. Clark, Eric Liu +1
We consider parallel global optimization of derivative-free expensive-to-evaluate functions, and propose an efficient method based on stochastic approximation for implementing a co…
Bayesian optimization for materials design
Peter I. Frazier, Jialei Wang
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian…
Clustering via Content-Augmented Stochastic Blockmodels
J. Massey Cashore, Xiaoting Zhao, Alexander A. Alemi +2
Much of the data being created on the web contains interactions between users and items. Stochastic blockmodels, and other methods for community detection and clustering of biparti…
Distance Dependent Infinite Latent Feature Models
Samuel J. Gershman, Peter I. Frazier, David M. Blei
Latent feature models are widely used to decompose data into a small number of components. Bayesian nonparametric variants of these models, which use the Indian buffet process (IBP…
Distance Dependent Chinese Restaurant Processes
David M. Blei, Peter I. Frazier
We develop the distance dependent Chinese restaurant process (CRP), a flexible class of distributions over partitions that allows for non-exchangeability. This class can be used to…