Abstract:

This paper studies the general framework of representation learning and develops a frequentist model averaging framework to combine different representation learning methods. The weight choice criterion is K-fold cross-validation criterion. We prove the asymptotic optimality and the weight consistency. Numerical studies and real-data analysis demonstrate the superior performance of the proposed method.

Short bio:

Xinyu Zhang, Professor in Academy of Mathematics and Systems Science, CAS. His research is on model averaging and its interdisciplinary applications in economic forecasting, management statistics, and machine learning. He has published some papers in mainstream journals of statistics and econometrics.

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