Optimistic Robust Optimization With Applications To Machine Learning

Robust Optimization has traditionally taken a pessimistic, or worst-case viewpoint of uncertainty which is motivated by a desire to find sets of optimal policies that maintain feasibility under a variety of operating conditions. In this paper, we explore an optimistic, or best-case view of uncertain...

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Veröffentlicht in:	arXiv.org 2017-11
Hauptverfasser:	Norton, Matthew, Takeda, Akiko, Mafusalov, Alexander
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Economic models Linear programming Machine learning Nonlinear programming Optimization Regularization Robustness Uncertainty
Online-Zugang:	Volltext
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