Paper Info
Title
The Bayesian Prophet: A Low-Regret Framework for Online Decision Making
Abstract
Motivated by the success of using black-box predictive algorithms as subroutines for online decision-making, we develop a new framework for designing online policies given access to an oracle providing statistical information about an offline benchmark. Having access to such prediction oracles enables simple and natural Bayesian selection policies, and raises the question as to how these policies perform in different settings. Our work makes two important contributions towards tackling this question: First, we develop a general technique we call *compensated coupling* which can be used to derive bounds on the expected regret (i.e., additive loss with respect to a benchmark) for any online policy and offline benchmark; Second, using this technique, we show that the Bayes Selector has constant expected regret (i.e., independent of the number of arrivals and resource levels) in any online packing and matching problem with a finite type-space. Our results generalize and simplify many existing results for online packing and matching problems, and suggest a promising pathway for obtaining oracle-driven policies for other online decision-making settings.
Year
DOI
Venue
2019
10.1145/3376930.3376982
Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
Keywords
Field
DocType
approximate dynamic programming, online packing., prophet inequalities, revenue management, stochastic optimization
Discrete mathematics,Regret,Subroutine,Predictive analytics,Oracle,Artificial intelligence,Online decision making,Machine learning,Mathematics,Bayesian probability,Bayes' theorem
Journal
Volume
Issue
ISSN
47
1
0163-5999
ISBN
Citations 
PageRank 
978-1-4503-6678-6
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Alberto Vera100.68
Siddhartha Banerjee218522.85