Hedging the Drift: Learning to Optimize Under Nonstationarity
Hedging the Drift: Learning to Optimize Under Nonstationarity
About this item
Full title
Author / Creator
Publisher
Linthicum: INFORMS
Journal title
Language
English
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Publication information
Publisher
Linthicum: INFORMS
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Scope and Contents
Contents
We introduce data-driven decision-making algorithms that achieve state-of-the-art dynamic regret bounds for a collection of nonstationary stochastic bandit settings. These settings capture applications such as advertisement allocation, dynamic pricing, and traffic network routing in changing environments. We show how the difficulty posed by the (un...
Alternative Titles
Full title
Hedging the Drift: Learning to Optimize Under Nonstationarity
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2645532697
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2645532697
Other Identifiers
ISSN
0025-1909
E-ISSN
1526-5501
DOI
10.1287/mnsc.2021.4024
