Optimal Rates For Regularization Of Statistical Inverse Learning Problems

14 April 2016

Papers citing "Optimal Rates For Regularization Of Statistical Inverse Learning Problems"

48 / 98 papers shown

Title
Data splitting improves statistical performance in overparametrized regimes Nicole Mücke Enrico Reiss Jonas Rungenhagen Markus Klein 60 8 0 21 Oct 2021
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? Dominic Richards Yan Sun Patrick Rebeschini 74 3 0 26 Aug 2021
Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization Gaspard Beugnot Julien Mairal Alessandro Rudi 55 1 0 16 Jun 2021
Learning the optimal Tikhonov regularizer for inverse problems Giovanni S. Alberti Ernesto De Vito Matti Lassas Luca Ratti Matteo Santacesaria 76 30 0 11 Jun 2021
From inexact optimization to learning via gradient concentration Bernhard Stankewitz Nicole Mücke Lorenzo Rosasco 88 5 0 09 Jun 2021
Learning particle swarming models from data with Gaussian processes Jinchao Feng Charles Kulick Yunxiang Ren Sui Tang 79 6 0 04 Jun 2021
Two-layer neural networks with values in a Banach space Yury Korolev 108 24 0 05 May 2021
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression Wei Cao Bing-Yi Jing 57 6 0 20 Apr 2021
Convex regularization in statistical inverse learning problems T. Bubba Martin Burger T. Helin Luca Ratti 56 10 0 18 Feb 2021
Online nonparametric regression with Sobolev kernels O. Zadorozhnyi Pierre Gaillard Sébastien Gerchinovitz Alessandro Rudi 47 4 0 06 Feb 2021
Nonparametric approximation of conditional expectation operators Mattes Mollenhauer P. Koltai 95 17 0 23 Dec 2020
Stochastic Gradient Descent Meets Distribution Regression Nicole Mücke 62 5 0 24 Oct 2020
Decentralised Learning with Random Features and Distributed Gradient Descent Dominic Richards Patrick Rebeschini Lorenzo Rosasco 67 18 0 01 Jul 2020
Optimal Rates of Distributed Regression with Imperfect Kernels Hongwei Sun Qiang Wu 30 15 0 30 Jun 2020
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime Atsushi Nitanda Taiji Suzuki 91 41 0 22 Jun 2020
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping Nicole Mücke Enrico Reiss 47 7 0 18 Jun 2020
Interpolation and Learning with Scale Dependent Kernels Nicolò Pagliana Alessandro Rudi Ernesto De Vito Lorenzo Rosasco 91 8 0 17 Jun 2020
Construction and Monte Carlo estimation of wavelet frames generated by a reproducing kernel Ernesto De Vito Ž. Kereta Valeriya Naumova Lorenzo Rosasco Stefano Vigogna 74 3 0 17 Jun 2020
Sample complexity and effective dimension for regression on manifolds Andrew D. McRae Justin Romberg Mark A. Davenport 106 8 0 13 Jun 2020
Lower bounds for invariant statistical models with applications to principal component analysis Martin Wahl 32 5 0 14 May 2020
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms Alain Celisse Martin Wahl 65 18 0 17 Apr 2020
A Spectral Analysis of Dot-product Kernels M. Scetbon Zaïd Harchaoui 419 2 0 28 Feb 2020
Inverse learning in Hilbert scales Abhishake Rastogi Peter Mathé 23 6 0 24 Feb 2020
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings Junhyung Park Krikamol Muandet 124 84 0 10 Feb 2020
Tikhonov regularization with oversmoothing penalty for nonlinear statistical inverse problems Abhishake Rastogi 58 5 0 01 Feb 2020
On the Improved Rates of Convergence for Matérn-type Kernel Ridge Regression, with Application to Calibration of Computer Models Rui Tuo Yan Wang C. F. Jeff Wu 67 28 0 01 Jan 2020
Implicit Regularization of Accelerated Methods in Hilbert Spaces Nicolò Pagliana Lorenzo Rosasco 103 18 0 30 May 2019
Lepskii Principle in Supervised Learning Gilles Blanchard Peter Mathé Nicole Mücke 55 12 0 26 May 2019
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up Dominic Richards Patrick Rebeschini 69 13 0 08 May 2019
Beating SGD Saturation with Tail-Averaging and Minibatching Nicole Mücke Gergely Neu Lorenzo Rosasco 106 37 0 22 Feb 2019
Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems Abhishake Rastogi Gilles Blanchard Peter Mathé 28 8 0 14 Feb 2019
The empirical process of residuals from an inverse regression T. Kutta N. Bissantz J. Chown Holger Dette MedIm 49 2 0 09 Feb 2019
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance Ulysse Marteau-Ferey Dmitrii Ostrovskii Francis R. Bach Alessandro Rudi 201 52 0 08 Feb 2019
A note on the prediction error of principal component regression Yana B. Feygin 115 6 0 07 Nov 2018
Kernel Conjugate Gradient Methods with Random Projections Bailey Kacsmar Douglas R Stinson 61 4 0 05 Nov 2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes Loucas Pillaud-Vivien Alessandro Rudi Francis R. Bach 185 103 0 25 May 2018
Adaptivity for Regularized Kernel Methods by Lepskii's Principle Nicole Mücke 17 3 0 15 Apr 2018
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces Junhong Lin Volkan Cevher 32 9 0 12 Mar 2018
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms Junhong Lin Volkan Cevher 82 34 0 22 Jan 2018
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces Junhong Lin Alessandro Rudi Lorenzo Rosasco Volkan Cevher 185 99 0 20 Jan 2018
Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods Gilles Blanchard O. Zadorozhnyi 47 7 0 05 Dec 2017
Reducing training time by efficient localized kernel regression Nicole Mücke 54 11 0 11 Jul 2017
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm Simon Fischer Ingo Steinwart 206 152 0 23 Feb 2017
Kernel regression, minimax rates and effective dimensionality: beyond the regular case Gilles Blanchard Nicole Mücke 53 9 0 12 Nov 2016
Optimal rates for the regularized learning algorithms under general source condition Abhishake Rastogi Sivananthan Sampath 77 31 0 07 Nov 2016
Parallelizing Spectral Algorithms for Kernel Learning Gilles Blanchard Nicole Mücke 67 15 0 24 Oct 2016
Convergence rates of Kernel Conjugate Gradient for random design regression Gilles Blanchard Nicole E. Kramer 81 38 0 08 Jul 2016
Weighted Residuals for Very Deep Networks Lee H. Dicker Daniel J. Hsu 32 47 0 28 May 2016