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Collective Model Fusion for Multiple Black-Box Experts
Minh Hoang · Nghia Hoang · Bryan Kian Hsiang Low · Carleton Kingsford

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #184

Model fusion is a fundamental problem in collec-tive machine learning (ML) where independentexperts with heterogeneous learning architecturesare required to combine expertise to improve pre-dictive performance. This is particularly chal-lenging in information-sensitive domains whereexperts do not have access to each other’s internalarchitecture and local data. This paper presentsthe first collective model fusion framework formultiple experts with heterogeneous black-box ar-chitectures. The proposed method will enable thisby addressing the key issues of how black-boxexperts interact to understand the predictive be-haviors of one another; how these understandingscan be represented and shared efficiently amongthemselves; and how the shared understandingscan be combined to generate high-quality consen-sus prediction. The performance of the resultingframework is analyzed theoretically and demon-strated empirically on several datasets.

Author Information

Minh Hoang (Carnegie Mellon University)
Nghia Hoang (MIT-IBM Watson AI Lab, IBM Research)
Bryan Kian Hsiang Low (National University of Singapore)
Carleton Kingsford (Carnegie Mellon University)

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