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Budgeted Optimization with Concurrent Stochastic-Duration Experiments

TitleBudgeted Optimization with Concurrent Stochastic-Duration Experiments
Publication TypeConference Paper
Year of Publication2011
AuthorsAzimi, J., A. Fern, and X. Z. Fern
Secondary AuthorsShawe-Taylor, J., R. S. Zemel, P. Bartlett, F C N. Pereira, and K. Q. Weinberger
Conference NameAdvances in Neural Information Processing Systems 24
Pagination1098–1106
Date Published12/2011
Conference LocationGranada, Spain
Abstract

Budgeted optimization involves optimizing an unknown function that is costly to evaluate by requesting a limited number of function evaluations at intelligently selected inputs. Typical problem formulations assume that experiments are selected one at a time with a limited total number of experiments, which fail to capture important aspects of many real-world problems. This paper defines a novel problem formulation with the following important extensions: 1) allowing for concurrent experiments; 2) allowing for stochastic experiment durations; and 3) placing constraints on both the total number of experiments and the total experimental time. We develop both offline and online algorithms for selecting concurrent experiments in this new setting and provide experimental results on a number of optimization benchmarks. The results show that our algorithms produce highly effective schedules compared to natural baselines.