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Poster
Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing
Farshad Lahouti · Babak Hassibi

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #30
in Tuesday Posters »

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.

Keywords: Information Theory · Learning Theory
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Author Information

Farshad Lahouti (Caltech)
Babak Hassibi (Caltech)

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