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AAAI
2010

Myopic Policies for Budgeted Optimization with Constrained Experiments

14 years 22 days ago
Myopic Policies for Budgeted Optimization with Constrained Experiments
Motivated by a real-world problem, we study a novel budgeted optimization problem 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 precise experimental setup. Rather, we may request a constrained experiment, which is a subset r of the input space for which the experimenter returns x r and f(x). Importantly, as the constraints become looser, the experimental cost decreases, but the uncertainty about the location x of the next observation increases. Our goal is to manage this trade-off by selecting a sequence of constrained experiments to best optimize f within the budget. We introduce cost-sensitive policies for selecting constrained experiments using both model-free and model-based approaches, inspired by policies for unconstrained settings. Experiments on synthetic functions and functions derived from real-world experimental data indicate that ou...
Javad Azimi, Xiaoli Fern, Alan Fern, Elizabeth Bur
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where AAAI
Authors Javad Azimi, Xiaoli Fern, Alan Fern, Elizabeth Burrows, Frank Chaplen, Yanzhen Fan, Hong Liu, Jun Jaio, Rebecca Schaller
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