- Gilles Blanchard, Nicole Mücke, “Optimal Rates for Regularization of Statistical Inverse Learning Problems”, Found Comput Math, 18, no. 4, 2018, 971
- Gérard Kerkyacharian, Mathilde Mougeot, Dominique Picard, Karine Tribouley, Multiscale, Nonlinear and Adaptive Approximation, 2009, 295
- Bastian Bohn, Michael Griebel, “Error Estimates for Multivariate Regression on Discretized Function Spaces”, SIAM J. Numer. Anal., 55, no. 4, 2017, 1843
- Andreas Hofinger, Friedrich Pillichshammer, “Learning a function from noisy samples at a finite sparse set of points”, Journal of Approximation Theory, 161, no. 2, 2009, 448
- Albert Cohen, Giovanni Migliorati, Fabio Nobile, “Discrete Least-Squares Approximations over Optimized Downward Closed Polynomial Spaces in Arbitrary Dimension”, Constr Approx, 45, no. 3, 2017, 497
- Ha Quang Minh, “Some Properties of Gaussian Reproducing Kernel Hilbert Spaces and Their Implications for Function Approximation and Learning Theory”, Constr Approx, 32, no. 2, 2010, 307
- G. Kerkyacharian, A. B. Tsybakov, V. Temlyakov, D. Picard, V. Koltchinskii, “Optimal Exponential Bounds on the Accuracy of Classification”, Constr Approx, 39, no. 3, 2014, 421
- Martin Eigel, Reinhold Schneider, Philipp Trunschke, Sebastian Wolf, “Variational Monte Carlo—bridging concepts of machine learning and high-dimensional partial differential equations”, Adv Comput Math, 45, no. 5-6, 2019, 2503
- 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, no. 8, 2024, 6291
- Zheng-Chu Guo, Ding-Xuan Zhou, “Concentration estimates for learning with unbounded sampling”, Adv Comput Math, 38, no. 1, 2013, 207