Stable Learning via Sample Reweighting


  • Zheyan Shen Tsinghua University
  • Peng Cui Tsinghua University
  • Tong Zhang The Hong Kong University of Science and Technology
  • Kun Kunag Tsinghua University & Zhejiang University



We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of parameter estimation, resulting in instability of prediction results when training and test distributions do not match. In this paper we theoretically analyze this fundamental problem and propose a sample reweighting method that reduces collinearity among input variables. Our method can be seen as a pretreatment of data to improve the condition of design matrix, and it can then be combined with any standard learning method for parameter estimation and variable selection. Empirical studies on both simulation and real datasets demonstrate the effectiveness of our method in terms of more stable performance across different distributed data.




How to Cite

Shen, Z., Cui, P., Zhang, T., & Kunag, K. (2020). Stable Learning via Sample Reweighting. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5692-5699.



AAAI Technical Track: Machine Learning