• Corpus ID: 219260854

Learning with CVaR-based feedback under potentially heavy tails

@article{Holland2020LearningWC,
  title={Learning with CVaR-based feedback under potentially heavy tails},
  author={Matthew J. Holland and El Mehdi Haress},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.02001}
}
We study learning algorithms that seek to minimize the conditional value-at-risk (CVaR), when all the learner knows is that the losses incurred may be heavy-tailed. We begin by studying a general-purpose estimator of CVaR for potentially heavy-tailed random variables, which is easy to implement in practice, and requires nothing more than finite variance and a distribution function that does not change too fast or slow around just the quantile of interest. With this estimator in hand, we then… 

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