A tutorial on Bayesian optimization
PI Frazier - arxiv preprint arxiv:1807.02811, 2018 - arxiv.org
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 domains of …
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
Review on ranking and selection: A new perspective
In this paper, we briefly review the development of ranking and selection (R&S) in the past
70 years, especially the theoretical achievements and practical applications in the past 20 …
70 years, especially the theoretical achievements and practical applications in the past 20 …
Bayesian optimization
PI Frazier - Recent advances in optimization and modeling …, 2018 - pubsonline.informs.org
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 domains of …
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
Simple bayesian algorithms for best arm identification
D Russo - Conference on Learning Theory, 2016 - proceedings.mlr.press
This paper considers the optimal adaptive allocation of measurement effort for identifying the
best among a finite set of options or designs. An experimenter sequentially chooses designs …
best among a finite set of options or designs. An experimenter sequentially chooses designs …
Customer acquisition via display advertising using multi-armed bandit experiments
Firms using online advertising regularly run experiments with multiple versions of their ads
since they are uncertain about which ones are most effective. During a campaign, firms try to …
since they are uncertain about which ones are most effective. During a campaign, firms try to …
[LLIBRE][B] Stochastic simulation optimization: an optimal computing budget allocation
CH Chen, LH Lee - 2011 - books.google.com
With the advance of new computing technology, simulation is becoming very popular for
designing large, complex and stochastic engineering systems, since closed-form analytical …
designing large, complex and stochastic engineering systems, since closed-form analytical …
Monte Carlo sampling-based methods for stochastic optimization
This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …
optimization problems. Such methods are required when—as it often happens in practice …
A knowledge-gradient policy for sequential information collection
In a sequential Bayesian ranking and selection problem with independent normal
populations and common known variance, we study a previously introduced measurement …
populations and common known variance, we study a previously introduced measurement …
The knowledge-gradient policy for correlated normal beliefs
We consider a Bayesian ranking and selection problem with independent normal rewards
and a correlated multivariate normal belief on the mean values of these rewards. Because …
and a correlated multivariate normal belief on the mean values of these rewards. Because …
Simulation optimization: a review, new developments, and applications
We provide a descriptive review of the main approaches for carrying out simulation
optimization, and sample some recent algorithmic and theoretical developments in …
optimization, and sample some recent algorithmic and theoretical developments in …