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The oracle Complexity of Smooth Convex Optimization in Nonstandard Settings
Cristóbal Guzmán
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Oznake: computer science;machine learning;active learning;computational learning theory;on-line learning;reinforcement learning;semi-supervised learning
First-order convex minimization algorithms are currently the methods of choice for large-scale sparse – and more generally parsimonious – regression models. We pose the question on the limits of performance of black-box oriented methods for convex minimization in non-standard settings, where the ...
Leto: 2015 Vir: videolectures.net
Št. zadetkov: 1
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