• Corpus ID: 14475218

From Dual to Primal Sub-optimality for Regularized Empirical Risk Minimization

@inproceedings{Lee2016FromDT,
  title={From Dual to Primal Sub-optimality for Regularized Empirical Risk Minimization},
  author={Ching-pei Lee},
  year={2016}
}
Regularized empirical risk minimization problems are fundamental tasks in machine learning and data analysis. Many successful approaches for solving these problems are based on a dual formulation, which often admits more efficient algorithms. Often, though, the primal solution is needed. In the case of regularized empirical risk minimization, there is a convenient formula for reconstructing an approximate primal solution from the approximate dual solution. However, the question of quantifying… 
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