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Srikant, Rayadurgam Kong, Seo Taek 2019-08-23T20:35:53Z 2019-08-23T20:35:53Z 2021-08-24T09:15:24Z 2019-04-15 2019-05 This thesis considers the multi-armed bandit (MAB) problem, both the traditional bandit feedback and graphical bandits when there is side information. Motivated by the Boltzmann exploration algorithm often used in the more general context of reinforcement learning, we present Almost Boltzmann Exploration (ABE) which fixes the under-exploration issue while maintaining an expression similar to Boltzmann exploration. We then present some real world applications of the MAB framework, comparing the performance of ABE with other bandit algorithms on real world datasets. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01 The student, Seo Taek Kong, accepted the attached license on 2019-04-12 at 19:06. The student, Seo Taek Kong, submitted this Thesis for approval on 2019-04-12 at 19:11. This Thesis was approved for publication on 2019-04-15 at 11:16. DSpace SAF Submission Ingestion Package generated from Vireo submission #13604 on 2019-08-22 at 15:06:13 Made available in DSpace on 2019-08-23T20:35:53Z (GMT). No. of bitstreams: 2 KONG-THESIS-2019.pdf: 750641 bytes, checksum: 22ccfd392702c28558d86907fb7903bf (MD5) LICENSE.txt: 4210 bytes, checksum: b110ca055d9d94ba391dea639db032f1 (MD5) Previous issue date: 2019-04-15 Embargo set by: Seth Robbins for item 112138 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 112138 on 2021-08-24T09:15:24Z. application/pdf http://hdl.handle.net/2142/105019 en Copyright 2019 Seo Taek Kong Multi-Armed Bandits, Boltzmann Exploration Multi-armed bandits and applications to large datasets text text Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.