Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm

23 February 2017

Papers citing "Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm"

50 / 105 papers shown

Title
Sharp Spectral Rates for Koopman Operator Learning Vladimir Kostic Karim Lounici P. Novelli Massimiliano Pontil 40 20 0 03 Feb 2023
Bandit Convex Optimisation Revisited: FTRL Achieves $\tilde{O}(t^{1/2})$ Regret David Young D. Leith Georgios Iosifidis 21 0 0 01 Feb 2023
Continuous Spatiotemporal Transformers Antonio H. O. Fonseca E. Zappala J. O. Caro David van Dijk 26 7 0 31 Jan 2023
Efficient Conditionally Invariant Representation Learning Roman Pogodin Namrata Deka Yazhe Li Danica J. Sutherland Victor Veitch Arthur Gretton BDL OOD CML 41 16 0 16 Dec 2022
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 30 9 0 20 Nov 2022
Minimax Optimal Kernel Operator Learning via Multilevel Training Jikai Jin Yiping Lu Jose H. Blanchet Lexing Ying 31 12 0 28 Sep 2022
Nonparametric augmented probability weighting with sparsity Xin He Xiaojun Mao Zhonglei Wang 28 0 0 28 Sep 2022
Quantitative limit theorems and bootstrap approximations for empirical spectral projectors M. Jirak Martin Wahl 6 4 0 26 Aug 2022
Improved Rates of Bootstrap Approximation for the Operator Norm: A Coordinate-Free Approach Miles E. Lopes 27 3 0 05 Aug 2022
Optimal Rates for Regularized Conditional Mean Embedding Learning Zhu Li Dimitri Meunier Mattes Mollenhauer Arthur Gretton 35 47 0 02 Aug 2022
Fast Instrument Learning with Faster Rates Ziyu Wang Yuhao Zhou Jun Zhu 29 3 0 22 May 2022
A Case of Exponential Convergence Rates for SVM Vivien A. Cabannes Stefano Vigogna 21 2 0 20 May 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 38 7 0 15 May 2022
Optimal Learning Rates for Regularized Least-Squares with a Fourier Capacity Condition Prem M. Talwai D. Simchi-Levi 11 2 0 16 Apr 2022
Dimensionality Reduction and Wasserstein Stability for Kernel Regression Stephan Eckstein Armin Iske Mathias Trabs 24 4 0 17 Mar 2022
Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data Umberto M. Tomasini Antonio Sclocchi M. Wyart 17 12 0 07 Feb 2022
The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods Nikhil Ghosh Song Mei Bin Yu 33 20 0 13 Nov 2021
Nyström Regularization for Time Series Forecasting Zirui Sun Mingwei Dai Yao Wang Shao-Bo Lin AI4TS 25 2 0 13 Nov 2021
Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection Rahul Singh 28 1 0 09 Nov 2021
Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves Rahul Singh Liyuan Xu Arthur Gretton 32 3 0 06 Nov 2021
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings M. Scetbon Laurent Meunier Yaniv Romano 28 10 0 28 Oct 2021
Learning curves for Gaussian process regression with power-law priors and targets Hui Jin P. Banerjee Guido Montúfar 14 17 0 23 Oct 2021
Quasi-Bayesian Dual Instrumental Variable Regression Ziyun Wang Yuhao Zhou Tongzheng Ren Jun Zhu 22 2 0 16 Jun 2021
On the Sample Complexity of Learning under Invariance and Geometric Stability A. Bietti Luca Venturi Joan Bruna 30 5 0 14 Jun 2021
The Inductive Bias of Quantum Kernels Jonas M. Kubler Simon Buchholz Bernhard Schölkopf 19 119 0 07 Jun 2021
Learning Curves for SGD on Structured Features Blake Bordelon Cengiz Pehlevan MLT 25 0 0 04 Jun 2021
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime Hugo Cui Bruno Loureiro Florent Krzakala Lenka Zdeborová 40 82 0 31 May 2021
Sobolev Norm Learning Rates for Conditional Mean Embeddings Prem M. Talwai A. Shameli D. Simchi-Levi 29 10 0 16 May 2021
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression Wei Cao Bing-Yi Jing 13 6 0 20 Apr 2021
Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension Rahul Singh CML 22 8 0 22 Feb 2021
Explaining Neural Scaling Laws Yasaman Bahri Ethan Dyer Jared Kaplan Jaehoon Lee Utkarsh Sharma 27 250 0 12 Feb 2021
Online nonparametric regression with Sobolev kernels O. Zadorozhnyi Pierre Gaillard Sébastien Gerchinovitz Alessandro Rudi 16 3 0 06 Feb 2021
Fast rates in structured prediction Vivien A. Cabannes Alessandro Rudi Francis R. Bach 20 19 0 01 Feb 2021
Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization Zhuoran Yang Yufeng Zhang Yongxin Chen Zhaoran Wang OT 38 5 0 21 Dec 2020
Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments Rahul Singh CML 39 39 0 18 Dec 2020
Stochastic Gradient Descent Meets Distribution Regression Nicole Mücke 36 5 0 24 Oct 2020
Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves Rahul Singh Liyuan Xu Arthur Gretton OffRL 68 27 0 10 Oct 2020
Kernel regression in high dimensions: Refined analysis beyond double descent Fanghui Liu Zhenyu Liao Johan A. K. Suykens 11 50 0 06 Oct 2020
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping Nicole Mücke Enrico Reiss 4 7 0 18 Jun 2020
How isotropic kernels perform on simple invariants J. Paccolat S. Spigler M. Wyart 27 4 0 17 Jun 2020
Estimates on Learning Rates for Multi-Penalty Distribution Regression Zhan Yu D. Ho 4 0 0 16 Jun 2020
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model Raphael Berthier Francis R. Bach Pierre Gaillard 18 37 0 15 Jun 2020
Sample complexity and effective dimension for regression on manifolds Andrew D. McRae Justin Romberg Mark A. Davenport 8 8 0 13 Jun 2020
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators Zejian Liu Meng Li 24 8 0 02 Jun 2020
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms Alain Celisse Martin Wahl 11 18 0 17 Apr 2020
A Spectral Analysis of Dot-product Kernels M. Scetbon Zaïd Harchaoui 200 2 0 28 Feb 2020
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness George Wynne F. Briol Mark Girolami 11 55 0 29 Jan 2020
On the Inductive Bias of Neural Tangent Kernels A. Bietti Julien Mairal 28 253 0 29 May 2019
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration Kwang-Sung Jun Ashok Cutkosky Francesco Orabona 19 20 0 25 May 2019
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance Ulysse Marteau-Ferey Dmitrii Ostrovskii Francis R. Bach Alessandro Rudi 30 52 0 08 Feb 2019