Learning Theory: An Approximation Theory Viewpoint: Estimating covering numbers

By Felipe Cucker, Ding-Xuan Zhou. 2007

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Geometry on Probability Spaces (2009, unknown): “See [10] [11].”
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning (2016, background): “Kakade et al. (2012) take advantage of the strongly-convex nature of certain matrix-norm regularizers to easily obtain generalization bounds for a variety of machine learning problems. Part of their work is devoted to the realm of online and off-line MTL. In the latter case, which pertains to the fo”
Regularization in kernel learning (2010, background): “Indeed, a result of Smale and Zhou [30] (see also [10]) allows us to bound the approximation error in terms of σ, as follows.”