Smoothness results of single and double layer solutions of the Helmholtz equations

By Tzu-Chu Lin. 1988

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On the optimal estimation of probability measures in weak and strong topologies (2013, neutral): “This generalization (see [25, 29] for a similar result to bound the expected suprema of empirical processes) allows to handle the polynomial growth of entropy number for β ≥ 1 compared to [30, Theorem 7].”
Variance-based Regularization with Convex Objectives (2016, background): “As evidenced by the substantial work on Rademacher- and Gaussian-complexity and symmetrization, in some instances covering-number-based arguments do not provide the sharpest scaling [2, 3, 43]; thus, in Section 3.”
Distributed stochastic optimization and learning (2014, background): “But SGD is also optimal, in a similar way, for smooth [1], [26], [24], and for strongly convex objectives (see below).”
Agnostic Selective Classification (2011, unknown): “5), but also if the disagreement coefficient grows slowly with respect to 1/ε (as shown by Wang [12], under sufficient smoothness conditions).”