2017/01/30 by Angela Zhou, Zhou, Angela, Irineo Cabreros +3
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Auction Theory and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mobile Crowdsensing and Crowdsourcing
paper · pdf · doi:10.48550/arxiv.1701.08795
openalex publication_date 2017/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an optimized worker assignment method allows us to boost the efficacy of any popular crowdsourcing estimation algorithm. We consider a mutual information interpretation of the crowdsourcing problem, which leads to a stochastic subset selection problem with a submodular objective function. We present experimental simulation results which demonstrate the effectiveness of our dynamic task allocation method for achieving higher accuracy, possibly requiring fewer labels, as well as improving upon a previous method which is sensitive to the proportion of users to questions.