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Online Resource Allocation : = New Results on Bounded Regret and Fairness.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Online Resource Allocation :/
其他題名:	New Results on Bounded Regret and Fairness.
作者:	Chen, Guanting.
面頁冊數:	1 online resource (89 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: A.
Contained By:	Dissertations Abstracts International84-04A.
標題:	Patients. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29342290click for full text (PQDT)
ISBN:	9798351499185

Online Resource Allocation : = New Results on Bounded Regret and Fairness.
Chen, Guanting.

Online Resource Allocation :New Results on Bounded Regret and Fairness. - 1 online resource (89 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: A.

Thesis (Ph.D.)--Stanford University, 2022.

Includes bibliographical references

In this thesis, we consider an online resource allocation problem where a decision maker accepts or rejects incoming customer requests irrevocably in order to maximize expected reward given limited resources. At each time, a new order/customer/bid is revealed with a request of some resource(s) and a reward. We consider a stochastic setting where all the orders are i.i.d. sampled from an unknown distribution. Such formulation arises from many classic applications such as the canonical (quantitybased) network revenue management problem and the Adwords problem. While the literature on the topic mainly focuses on regret minimization, our paper considers the fairness aspect of the problem. On a high level, we define the fairness in a way that a fair online algorithm should treat similar agents/customers similarly, and the decision made for similar agents/customers should be consistent over time. To achieve this goal, we define the fair offline solution as the analytic center of the offline optimal solution set, and introduce cumulative unfairness as the cumulative deviation from the online solutions to the fair offline solution over time. We propose a fair algorithm based on an interior-point LP solver and a mechanism that dynamically detects unfair resource spending. Our algorithm achieves cumulative unfairness on the scale of order O(log(T)), while maintains the regret to be bounded without dependency on T. In addition, compared to the literature, our result is produced under less restrictive assumptions on the degeneracy of the underlying linear program.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798351499185Subjects--Topical Terms:

1961957
Patients.
Index Terms--Genre/Form:

542853
Electronic books.

Online Resource Allocation : = New Results on Bounded Regret and Fairness.
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502 $a Thesis (Ph.D.)--Stanford University, 2022.
504 $a Includes bibliographical references
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