Title	and CJ Ho: Bandits in Crowdsourcing
Creator	Liu, Yang
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2016-08-30T10:20
Description	This talk will overview some of the successes in connecting Multi-armed Bandit (MAB) algorithm to crowdsourcing research. With MAB being a powerful tool for decision making and machine learning tasks, there are some salient challenges when we adopt this framework for crowdsourcing studies, including large exploration space, budget constraint, and lack of ground-truth. We will survey several recent results that help overcome the above hurdles. Time permitting, we will go through a couple of examples that use a bandit framework as a reputation system to provide long-term incentives.
Extent	31 minutes
Subject	Mathematics; Game theory, economics, social and behavioral sciences; Computer science; Applied computer science
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Harvard
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2017-03-01
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0343032
URI	http://hdl.handle.net/2429/60752
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Postdoctoral
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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