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Synthesizing New Expertise via Collaboration

Mazaheri, Bijan and Jain, Siddharth and Bruck, Jehoshua (2021) Synthesizing New Expertise via Collaboration. In: 2021 IEEE International Symposium on Information Theory (ISIT). IEEE , Piscataway, NJ, pp. 2447-2452. ISBN 978-1-5386-8209-8.

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Consider a set of classes and an uncertain input. Suppose, we do not have access to data and only have knowledge of perfect experts between a few classes in the set. What constitutes a consistent set of opinions? How can we use this to predict the opinions of experts on missing sub-domains? In this paper, we define a framework to analyze this problem. In particular, we define an expert graph where vertices represent classes and edges represent binary experts on the topics of their vertices. We derive necessary conditions for an expert graph to be valid. Further, we show that these conditions are also sufficient if the graph is a cycle, which can yield unintuitive results. Using these conditions, we provide an algorithm to obtain upper and lower bounds on the weights of unknown edges in an expert graph.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper ItemTechnical Report
Mazaheri, Bijan0000-0001-9690-8686
Jain, Siddharth0000-0002-9164-6119
Bruck, Jehoshua0000-0001-8474-0812
Alternate Title:Expert Graphs: Synthesizing New Expertise via Collaboration
Additional Information:© 2021 IEEE.
Record Number:CaltechAUTHORS:20211110-153150519
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Official Citation:B. Mazaheri, S. Jain and J. Bruck, "Synthesizing New Expertise via Collaboration," 2021 IEEE International Symposium on Information Theory (ISIT), 2021, pp. 2447-2452, doi: 10.1109/ISIT45174.2021.9517822
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111815
Deposited By: Tony Diaz
Deposited On:11 Nov 2021 19:24
Last Modified:11 Nov 2021 19:24

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