Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees

26 April 2018

Papers citing "Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees"

45 / 95 papers shown

Title
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes Q. Hoang T. Hoang Hai Pham David P. Woodruff 6 5 0 17 Nov 2020
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens 8 4 0 03 Nov 2020
Kernel regression in high dimensions: Refined analysis beyond double descent Fanghui Liu Zhenyu Liao Johan A. K. Suykens 6 49 0 06 Oct 2020
Generalized Leverage Score Sampling for Neural Networks J. Lee Ruoqi Shen Zhao-quan Song Mengdi Wang Zheng Yu 21 42 0 21 Sep 2020
Benign Overfitting and Noisy Features Zhu Li Weijie Su Dino Sejdinovic 10 22 0 06 Aug 2020
Decentralised Learning with Random Features and Distributed Gradient Descent Dominic Richards Patrick Rebeschini Lorenzo Rosasco 11 18 0 01 Jul 2020
Training (Overparametrized) Neural Networks in Near-Linear Time Jan van den Brand Binghui Peng Zhao-quan Song Omri Weinstein ODL 21 81 0 20 Jun 2020
The Statistical Cost of Robust Kernel Hyperparameter Tuning R. A. Meyer Christopher Musco 16 2 0 14 Jun 2020
Fourier Sparse Leverage Scores and Approximate Kernel Learning T. Erdélyi Cameron Musco Christopher Musco 8 21 0 12 Jun 2020
A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, a Precise Phase Transition, and the Corresponding Double Descent Zhenyu Liao Romain Couillet Michael W. Mahoney 16 88 0 09 Jun 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 44 172 0 23 Apr 2020
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions H. Yamasaki Sathyawageeswar Subramanian Sho Sonoda M. Koashi 30 17 0 22 Apr 2020
An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications Haotian Jiang Y. Lee Zhao-quan Song Sam Chiu-wai Wong 14 106 0 08 Apr 2020
How Good are Low-Rank Approximations in Gaussian Process Regression? C. Daskalakis P. Dellaportas A. Panos 9 3 0 03 Apr 2020
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing Michael Kapralov Navid Nouri Ilya P. Razenshteyn A. Velingker A. Zandieh 24 13 0 21 Mar 2020
Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels Fatih Ilhan Suleyman Serdar Kozat 6 7 0 07 Mar 2020
Convolutional Spectral Kernel Learning Jian Li Yong Liu Weiping Wang BDL 4 5 0 28 Feb 2020
Sparse Recovery With Non-Linear Fourier Features Ayça Özçelikkale 6 5 0 12 Feb 2020
RFN: A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Spaces Ting-Jui Chang Shahin Shahrampour 12 2 0 12 Feb 2020
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features Liang Ding Rui Tuo Shahin Shahrampour 9 8 0 11 Feb 2020
COKE: Communication-Censored Decentralized Kernel Learning Ping Xu Yue Wang Xiang Chen Z. Tian 15 20 0 28 Jan 2020
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling Fanghui Liu Xiaolin Huang Yudong Chen Jie Yang Johan A. K. Suykens 19 21 0 20 Nov 2019
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features Shingo Yashima Atsushi Nitanda Taiji Suzuki 6 2 0 13 Nov 2019
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement Ali (Aliakbar) Panahi Seyran Saeedi Tom Arodz 6 29 0 12 Nov 2019
Importance Sampling via Local Sensitivity Anant Raj Cameron Musco Lester W. Mackey 10 6 0 04 Nov 2019
Gaussian Processes with Errors in Variables: Theory and Computation Shuang Zhou D. Pati Tianying Wang Yun Yang R. Carroll 16 3 0 14 Oct 2019
ORCCA: Optimal Randomized Canonical Correlation Analysis Yinsong Wang Shahin Shahrampour 6 5 0 11 Oct 2019
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping Shusen Wang 23 2 0 24 Sep 2019
On the Downstream Performance of Compressed Word Embeddings Avner May Jian Zhang Tri Dao Christopher Ré 19 27 0 03 Sep 2019
Statistical and Computational Trade-Offs in Kernel K-Means Daniele Calandriello Lorenzo Rosasco 11 32 0 27 Aug 2019
Sample Efficient Toeplitz Covariance Estimation Yonina C. Eldar Jerry Li Cameron Musco Christopher Musco 12 13 0 14 May 2019
On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees Shahin Shahrampour Soheil Kolouri 6 10 0 20 Mar 2019
Spatial Analysis Made Easy with Linear Regression and Kernels Philip Milton E. Giorgi Samir Bhatt 24 16 0 22 Feb 2019
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis K. Hayashi Masaaki Imaizumi Yuichi Yoshida 17 12 0 28 Jan 2019
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms H. Avron Michael Kapralov Cameron Musco Christopher Musco A. Velingker A. Zandieh 17 47 0 20 Dec 2018
The GaussianSketch for Almost Relative Error Kernel Distance J. M. Phillips W. Tai 17 1 0 09 Nov 2018
Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation Jian Zhang Avner May Tri Dao Christopher Ré 11 29 0 31 Oct 2018
Data-dependent compression of random features for large-scale kernel approximation Raj Agrawal Trevor Campbell Jonathan H. Huggins Tamara Broderick 11 20 0 09 Oct 2018
Generalization Properties of hyper-RKHS and its Applications Fanghui Liu Lei Shi Xiaolin Huang Jie-jin Yang Johan A. K. Suykens 13 4 0 26 Sep 2018
Towards A Unified Analysis of Random Fourier Features Zhu Li Jean-François Ton Dino Oglic Dino Sejdinovic 16 5 0 24 Jun 2018
Optimal Sketching Bounds for Exp-concave Stochastic Minimization Naman Agarwal Alon Gonen 17 0 0 21 May 2018
Active Regression via Linear-Sample Sparsification Xue Chen Eric Price 24 61 0 27 Nov 2017
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features Jean-François Ton Seth Flaxman Dino Sejdinovic Samir Bhatt GP 31 52 0 15 Nov 2017
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? Cameron Musco David P. Woodruff 20 13 0 05 Nov 2017
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 86 277 0 09 Aug 2012