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Model linkage selection for cooperative learning

Publication ,  Journal Article
Zhou, J; Ding, J; Tan, KM; Tarokh, V
Published in: Journal of Machine Learning Research
January 1, 2021

We consider the distributed learning setting where each agent or learner holds a specific parametric model and a data source. The goal is to integrate information across a set of learners and data sources to enhance the prediction accuracy of a given learner. A natural way to integrate information is to build a joint model across a group of learners that shares common parameters of interest. However, the underlying parameter sharing patterns across a set of learners may not be known a priori. Misspecifying the parameter sharing patterns or the parametric model for each learner often yields a biased estimator that degrades the prediction accuracy. We propose a general method to integrate information across a set of learners that is robust against misspecification of both models and parameter sharing patterns. The main crux of our proposed method is to sequentially incorporate additional learners that can enhance the prediction accuracy of an existing joint model based on user-specified parameter sharing patterns across a set of learners. Theoretically, we show that the proposed method can data-adaptively select a parameter sharing pattern that enhances the predictive performance of a given learner. Extensive numerical studies are conducted to assess the performance of the proposed method.

Duke Scholars

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

January 1, 2021

Volume

22

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences
 

Citation

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MLA
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Zhou, J., Ding, J., Tan, K. M., & Tarokh, V. (2021). Model linkage selection for cooperative learning. Journal of Machine Learning Research, 22.
Zhou, J., J. Ding, K. M. Tan, and V. Tarokh. “Model linkage selection for cooperative learning.” Journal of Machine Learning Research 22 (January 1, 2021).
Zhou J, Ding J, Tan KM, Tarokh V. Model linkage selection for cooperative learning. Journal of Machine Learning Research. 2021 Jan 1;22.
Zhou, J., et al. “Model linkage selection for cooperative learning.” Journal of Machine Learning Research, vol. 22, Jan. 2021.
Zhou J, Ding J, Tan KM, Tarokh V. Model linkage selection for cooperative learning. Journal of Machine Learning Research. 2021 Jan 1;22.

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

January 1, 2021

Volume

22

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences