Contextual Ranking and Selection with Gaussian Processes

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publication2021 Winter Simulation Conference (WSC)
PublisherIEEE
Number of pages12
ISBN (Electronic)9781665433112
ISBN (Print)978-1-6654-3312-9
Publication statusPublished - Dec 2021

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736
ISSN (Electronic)1558-4305

Conference

Title2021 Winter Simulation Conference (WSC 2021)
LocationJW Marriott Desert Ridge (Face-to-face & Virtual)
PlaceUnited States
CityPhoenix
Period13 - 15 December 2021

Abstract

In many real world problems, we are faced with the problem of selecting the best among a finite number of alternatives, where the best alternative is determined based on context specific information. In this work, we study the contextual Ranking and Selection problem under a finite arm-finite context setting, where we aim to find the best alternative for each context. We use a separate Gaussian process to model the reward for each arm, derive the large deviations rate function for both the expected and worst-case contextual probability of correct selection, and propose an iterative algorithm for maximizing the rate function. Numerical experiments show that our algorithm is highly competitive in terms of sampling efficiency, while having significantly smaller computational overhead.

Citation Format(s)

Contextual Ranking and Selection with Gaussian Processes. / Cakmak, Sait; Zhou, Enlu; Gao, Siyang.
2021 Winter Simulation Conference (WSC). IEEE, 2021. (Proceedings - Winter Simulation Conference).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review