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We propose a risk-averse statistical learning framework wherein the performance of a learning algorithm is evaluated by the conditional value-at-risk (CVaR) of losses rather than the expected loss.
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Renata Mansini, Włodzimierz Ogryczak, and M Grazia Speranza · 2007
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Sheena Yau, Roy H Kwon, J Scott Rogers, and Desheng Wu · 2011
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F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Alexander Shapiro, Darinka Dentcheva, and Andrzej Ruszczyński · 2014
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Algorithms for CVaR optimization in MDPs
Yinlam Chow and Mohammad Ghavamzadeh · 2014
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Aviv Tamar, Yonatan Glassner, and Shie Mannor · 2015
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