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Synthesizing New Expertise via Collaboration

Mazaheri, Bijan and Jain, Siddharth and Bruck, Jehoshua (2021) Synthesizing New Expertise via Collaboration. Parallel and Distributed Systems Group Technical Reports, etr150. California Institute of Technology , Pasadena, CA. (Unpublished)

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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:Report or Paper (Technical Report)
Related URLs:
URLURL TypeDescription Report ItemIEEE Conference Paper
Jain, Siddharth0000-0002-9164-6119
Bruck, Jehoshua0000-0001-8474-0812
Group:Parallel and Distributed Systems Group
Series Name:Parallel and Distributed Systems Group Technical Reports
Issue or Number:etr150
Record Number:CaltechAUTHORS:20210624-214158214
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109570
Deposited By: George Porter
Deposited On:24 Jun 2021 21:57
Last Modified:10 Nov 2021 15:33

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