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Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing

Lahouti, Farshad and Hassibi, Babak (2016) Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. Advances in Neural Information Processing Systems. No.29. Neural Information Processing Systems Foundation, Inc. , pp. 5065-5073. ISBN 978-1-5108-3881-9.

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Digital crowdsourcing (CS) is a modern approach to perform certain large projects using small contributions of a large crowd. In CS, a taskmaster typically breaks down the project into small batches of tasks and assigns them to so-called workers with imperfect skill levels. The crowdsourcer then collects and analyzes the results for inference and serving the purpose of the project. In this work, the CS problem, as a human-in-the-loop computation problem, is modeled and analyzed in an information theoretic rate-distortion framework. The purpose is to identify the ultimate fidelity that one can achieve by any form of query from the crowd and any decoding (inference) algorithm with a given budget. The results are established by a joint source channel (de)coding scheme, which represent the query scheme and inference, over parallel noisy channels, which model workers with imperfect skill levels. We also present and analyze a query scheme dubbed k-ary incidence coding and study optimized query pricing in this setting.

Item Type:Book Section
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URLURL TypeDescription Paper
Lahouti, Farshad0000-0002-8729-873X
Additional Information:© 2005 Neural Information Processing Systems Foundation, Inc.
Series Name:Advances in Neural Information Processing Systems
Issue or Number:29
Record Number:CaltechAUTHORS:20210105-133359837
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107319
Deposited By: George Porter
Deposited On:05 Jan 2021 22:54
Last Modified:06 Jan 2021 18:21

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