Myopic policies for budgeted optimization with constrained experiments

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Citeable URL: https://ir.library.oregonstate.edu/concern/defaults/4j03d454v

Descriptions

Attribute Name	Values
Creator	Lin, Wei
Abstract	Motivated by a real-world problem, we study a novel setting for budgeted optimization where the goal is to optimize an unknown function f(x) given a budget. In our setting, it is not practical to request samples of f(x) at precise input values due to the formidable cost of experimental setup at precise values. Rather, we may request constrained experiments, which give the experimenter constraints on x for which they must return f(x). Importantly, as the constraints become looser, the experimental cost decreases, but the uncertainty about the location of the next observation increases. Our problem is to manage this trade-off by selecting a sequence of constrained experiments to best optimize f within the budget. We propose a number of myopic policies for selecting constrained experiments using both model-free and model-based approaches, inspired by policies for unconstrained settings. Experiments on synthetic and real-world functions indicate that our policies outperform random selection, that the model-based policies are superior to model-free ones, and give insights into which policies are preferable overall. Graduation date: 2008 Keywords: Machine Learning, Budgeted Learning
Contributor	Fern, Alan Fern, Xiaoli
Resource Type	Research Paper
Date Issued	2008-12-01
Subject	Machine learning
Déclaration de droits	Copyright Not Evaluated
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]
Replaces	http://hdl.handle.net/1957/10151

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