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Adaptive Preference Learning with Ba...

ProQuest Information and Learning Co.

Adaptive Preference Learning with Bandit Feedback : = Information Filtering, Dueling Bandits and Incentivizing Exploration.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Adaptive Preference Learning with Bandit Feedback :/
Reminder of title:	Information Filtering, Dueling Bandits and Incentivizing Exploration.
Author:	Chen, Bangrui.
Description:	1 online resource (141 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
Subject:	Operations research. -
Online resource:	click for full text (PQDT)
ISBN:	9780355528138

Adaptive Preference Learning with Bandit Feedback : = Information Filtering, Dueling Bandits and Incentivizing Exploration.
Chen, Bangrui.

Adaptive Preference Learning with Bandit Feedback :Information Filtering, Dueling Bandits and Incentivizing Exploration. - 1 online resource (141 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--Cornell University, 2017.

Includes bibliographical references

In this thesis, we study adaptive preference learning, in which a machine learning system learns users' preferences from feedback while simultaneously using these learned preferences to help them find preferred items. We study three different types of user feedback in three application setting: cardinal feedback with application in information filtering systems, ordinal feedback with application in personalized content recommender systems, and attribute feedback with application in review aggregators. We connect these settings respectively to existing work on classical multi-armed bandits, dueling bandits, and incentivizing exploration. For each type of feedback and application setting, we provide an algorithm and a theoretical analysis bounding its regret. We demonstrate through numerical experiments that our algorithms outperform existing benchmarks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355528138Subjects--Topical Terms:

573517
Operations research.
Index Terms--Genre/Form:

554714
Electronic books.

Adaptive Preference Learning with Bandit Feedback : = Information Filtering, Dueling Bandits and Incentivizing Exploration.
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020 $a 9780355528138
035 $a (MiAaPQ)AAI10680541
035 $a (MiAaPQ)cornellgrad:10605
035 $a AAI10680541
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Chen, Bangrui. $3 1190684
245 1 0 $a Adaptive Preference Learning with Bandit Feedback : $b Information Filtering, Dueling Bandits and Incentivizing Exploration.
264 0 $c 2017
300 $a 1 online resource (141 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Peter I. Frazier.
502 $a Thesis (Ph.D.)--Cornell University, 2017.
504 $a Includes bibliographical references
520 $a In this thesis, we study adaptive preference learning, in which a machine learning system learns users' preferences from feedback while simultaneously using these learned preferences to help them find preferred items. We study three different types of user feedback in three application setting: cardinal feedback with application in information filtering systems, ordinal feedback with application in personalized content recommender systems, and attribute feedback with application in review aggregators. We connect these settings respectively to existing work on classical multi-armed bandits, dueling bandits, and incentivizing exploration. For each type of feedback and application setting, we provide an algorithm and a theoretical analysis bounding its regret. We demonstrate through numerical experiments that our algorithms outperform existing benchmarks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Operations research. $3 573517
650 4 $a Computer science. $3 573171
650 4 $a Statistics. $3 556824
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0796
690 $a 0984
690 $a 0463
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Cornell University. $b Operations Research. $3 1180996
773 0 $t Dissertation Abstracts International $g 79-03B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10680541 $z click for full text (PQDT)

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