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This content will become publicly available on July 31, 2024

Title: Optimal Budget Allocation for Crowdsourcing Labels for Graphs
Crowdsourcing is an effective and efficient paradigm for obtaining labels for unlabeled corpus employing crowd workers. This work considers the budget allocation problem for a generalized setting on a graph of instances to be labeled where edges encode instance dependencies. Specifically, given a graph and a labeling budget, we propose an optimal policy to allocate the budget among the instances to maximize the overall labeling accuracy. We formulate the problem as a Bayesian Markov Decision Process (MDP), where we define our task as an optimization problem that maximizes the overall label accuracy under budget constraints. Then, we propose a novel stage-wise reward function that considers the effect of worker labels on the whole graph at each timestamp. This reward function is utilized to find an optimal policy for the optimization problem. Theoretically, we show that our proposed policies are consistent when the budget is infinite. We conduct extensive experiments on five real-world graph datasets and demonstrate the effectiveness of the proposed policies to achieve a higher label accuracy under budget constraints.  more » « less
Award ID(s):
2008155
NSF-PAR ID:
10466695
Author(s) / Creator(s):
; ; ;
Editor(s):
Evans, Robin J.; Shpitser, Ilya
Publisher / Repository:
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence
Date Published:
Volume:
216
Page Range / eLocation ID:
1154-1163
Subject(s) / Keyword(s):
["crowdsourcing","Bayesian Markov Decision Process","budget allocation"]
Format(s):
Medium: X
Location:
PIttsburgh, PA, USA
Sponsoring Org:
National Science Foundation
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