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Lorenzo Rosasco | Alessandro Rudi | Nicolo Pagliana | Ernesto De Vito | L. Rosasco | Alessandro Rudi | Nicolò Pagliana | E. Vito
[1] A. Caponnetto,et al. Optimal Rates for the Regularized Least-Squares Algorithm , 2007, Found. Comput. Math..
[2] Gilles Blanchard,et al. Optimal Rates for Regularization of Statistical Inverse Learning Problems , 2016, Found. Comput. Math..
[3] Andrea Montanari,et al. Surprises in High-Dimensional Ridgeless Least Squares Interpolation , 2019, Annals of statistics.
[4] Lorenzo Rosasco,et al. On regularization algorithms in learning theory , 2007, J. Complex..
[5] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[6] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[7] V. Marčenko,et al. DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES , 1967 .
[8] Weifeng Liu,et al. Adaptive and Learning Systems for Signal Processing, Communication, and Control , 2010 .
[9] Adam Krzyzak,et al. A Distribution-Free Theory of Nonparametric Regression , 2002, Springer series in statistics.
[10] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[11] Andrea Montanari,et al. Linearized two-layers neural networks in high dimension , 2019, The Annals of Statistics.
[12] Ingo Steinwart,et al. A closer look at covering number bounds for Gaussian kernels , 2019, J. Complex..
[13] Tengyuan Liang,et al. Just Interpolate: Kernel "Ridgeless" Regression Can Generalize , 2018, The Annals of Statistics.
[14] Lorenzo Rosasco,et al. Elastic-net regularization in learning theory , 2008, J. Complex..
[15] Alexander Rakhlin,et al. Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon , 2018, COLT.
[16] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[17] Felipe Cucker,et al. On the mathematical foundations of learning , 2001 .
[18] A. Berlinet,et al. Reproducing kernel Hilbert spaces in probability and statistics , 2004 .
[19] J. T. Spooner,et al. Adaptive and Learning Systems for Signal Processing , Communications , and Control , 2013 .
[20] Mikhail Belkin,et al. Two models of double descent for weak features , 2019, SIAM J. Math. Data Sci..
[22] Gábor Lugosi,et al. Introduction to Statistical Learning Theory , 2004, Advanced Lectures on Machine Learning.
[23] Mikhail Belkin,et al. Approximation beats concentration? An approximation view on inference with smooth radial kernels , 2018, COLT.
[24] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[25] Lorenzo Rosasco,et al. On the Sample Complexity of Subspace Learning , 2013, NIPS.
[26] Mikhail Belkin,et al. To understand deep learning we need to understand kernel learning , 2018, ICML.
[27] Armin Iske,et al. Improved estimates for condition numbers of radial basis function interpolation matrices , 2017, J. Approx. Theory.
[28] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[29] Tengyuan Liang,et al. On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels , 2019, ArXiv.
[30] Mikhail Belkin,et al. Reconciling modern machine-learning practice and the classical bias–variance trade-off , 2018, Proceedings of the National Academy of Sciences.
[31] Mikhail Belkin,et al. Does data interpolation contradict statistical optimality? , 2018, AISTATS.
[32] Philip M. Long,et al. Benign overfitting in linear regression , 2019, Proceedings of the National Academy of Sciences.
[33] Ingo Steinwart,et al. Optimal regression rates for SVMs using Gaussian kernels , 2013 .
[34] Ding-Xuan Zhou,et al. Learning Theory: An Approximation Theory Viewpoint , 2007 .
[35] Don R. Hush,et al. Optimal Rates for Regularized Least Squares Regression , 2009, COLT.