Minimizing the Maximal Loss: How and Why?

@inproceedings{ShalevShwartz2016MinimizingTM,
  title={Minimizing the Maximal Loss: How and Why?},
  author={Shai Shalev-Shwartz and Yonatan Wexler},
  booktitle={ICML},
  year={2016}
}
A commonly used learning rule is to approximately minimize the \emph{average} loss over the training set. Other learning algorithms, such as AdaBoost and hard-SVM, aim at minimizing the \emph{maximal} loss over the training set. The average loss is more popular, particularly in deep learning, due to three main reasons. First, it can be conveniently minimized using online algorithms, that process few examples at each iteration. Second, it is often argued that there is no sense to minimize the… CONTINUE READING

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