S. V. Konyagin, E. D. Livshits, “On adaptive estimators in statistical learning theory”, Proc. Steklov Inst. Math., 260, № 1, 2008, 185
Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu, “Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel”, Neural Computation, 26, № 10, 2014, 2350
Rui Li, Youming Liu, “Wavelet Optimal Estimations for Density Functions under Severely Ill-Posed Noises”, Abstract and Applied Analysis, 2013, 2013, 1
Andreas Hofinger, Friedrich Pillichshammer, “Learning a function from noisy samples at a finite sparse set of points”, Journal of Approximation Theory, 161, № 2, 2009, 448
Shao-Bo Lin, “Nonparametric regression using needlet kernels for spherical data”, Journal of Complexity, 50, 2019, 66
Rui Li, YouMing Liu, “Supersmooth density estimations over L
p risk by wavelets”, Sci. China Math., 60, № 10, 2017, 1901
Gilles Blanchard, Nicole Mücke, “Optimal Rates for Regularization of Statistical Inverse Learning Problems”, Found Comput Math, 18, № 4, 2018, 971
Gilles Blanchard, Nicole Mücke, “Kernel regression, minimax rates and effective dimensionality: Beyond the regular case”, Anal. Appl., 18, № 04, 2020, 683
Andrew J. Kurdila, Sai Tej Paruchuri, Nathan Powell, Jia Guo, Parag Bobade, Boone Estes, Haoran Wang, “Approximation of discrete and orbital Koopman operators over subsets and manifolds”, Nonlinear Dyn, 112, № 8, 2024, 6291
Mark J. van der Laan, Sandrine Dudoit, Aad W. van der Vaart, “The cross-validated adaptive epsilon-net estimator”, Statistics & Decisions, 24, № 3, 2006, 373