Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/167750
Title: Collective Model Fusion for Multiple Black-Box Experts
Authors: HOANG QUANG MINH 
HOANG TRONG NGHIA 
LOW KIAN HSIANG 
Carleton Kingsford
Issue Date: 9-Jun-2019
Publisher: International Machine Learning Society (IMLS)
Citation: HOANG QUANG MINH, HOANG TRONG NGHIA, LOW KIAN HSIANG, Carleton Kingsford (2019-06-09). Collective Model Fusion for Multiple Black-Box Experts. 36th International Conference on Machine Learning : 2742-2750. ScholarBank@NUS Repository.
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Model fusion is a fundamental problem in collective machine learning (ML) where independent experts with heterogeneous learning architectures are required to combine expertise to improve predictive performance. This is particularly challenging in information-sensitive domains (e.g., medical records in health-care analytics) where experts do not have access to each other's internal architecture and local data. To address this challenge, this paper presents the first collective model fusion framework for multiple experts with heterogeneous black-box architectures. The proposed method will enable this by addressing the following key issues of how black-box experts interact to understand the predictive behaviors of one another; how these understandings can be represented and shared efficiently among themselves; and how the shared understandings can be combined to generate high-quality consensus prediction. The performance of the resulting framework is analyzed theoretically and demonstrated empirically on several datasets.
Source Title: 36th International Conference on Machine Learning
URI: https://scholarbank.nus.edu.sg/handle/10635/167750
ISBN: 9781510886988
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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