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Data-driven Random Fourier Features using Stein Effect

Data-driven Random Fourier Features using Stein Effect

23 May 2017
Wei-Cheng Chang
Chun-Liang Li
Yiming Yang
Barnabás Póczós
ArXiv (abs)PDFHTML

Papers citing "Data-driven Random Fourier Features using Stein Effect"

11 / 11 papers shown
Title
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian
  Processes
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
Liam Hodgkinson
Christopher van der Heide
Fred Roosta
Michael W. Mahoney
UQCV
77
6
0
14 Oct 2022
On The Relative Error of Random Fourier Features for Preserving Kernel
  Distance
On The Relative Error of Random Fourier Features for Preserving Kernel Distance
Kuan Cheng
S. Jiang
Luojian Wei
Zhide Wei
98
1
0
01 Oct 2022
Exponential Error Convergence in Data Classification with Optimized
  Random Features: Acceleration by Quantum Machine Learning
Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning
H. Yamasaki
Sho Sonoda
73
6
0
16 Jun 2021
Learning to Learn Kernels with Variational Random Features
Learning to Learn Kernels with Variational Random Features
Xiantong Zhen
Hao Sun
Yingjun Du
Jun Xu
Yilong Yin
Ling Shao
Cees G. M. Snoek
DRL
72
34
0
11 Jun 2020
Random Features for Kernel Approximation: A Survey on Algorithms,
  Theory, and Beyond
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
Fanghui Liu
Xiaolin Huang
Yudong Chen
Johan A. K. Suykens
BDL
126
176
0
23 Apr 2020
ORCCA: Optimal Randomized Canonical Correlation Analysis
ORCCA: Optimal Randomized Canonical Correlation Analysis
Yinsong Wang
Shahin Shahrampour
103
5
0
11 Oct 2019
Implicit Kernel Learning
Implicit Kernel Learning
Chun-Liang Li
Wei-Cheng Chang
Youssef Mroueh
Yiming Yang
Barnabás Póczós
VLM
76
42
0
26 Feb 2019
Data-dependent compression of random features for large-scale kernel
  approximation
Data-dependent compression of random features for large-scale kernel approximation
Raj Agrawal
Trevor Campbell
Jonathan H. Huggins
Tamara Broderick
51
20
0
09 Oct 2018
Learning Bounds for Greedy Approximation with Explicit Feature Maps from
  Multiple Kernels
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels
Shahin Shahrampour
Vahid Tarokh
41
6
0
09 Oct 2018
On Data-Dependent Random Features for Improved Generalization in
  Supervised Learning
On Data-Dependent Random Features for Improved Generalization in Supervised Learning
Shahin Shahrampour
Ahmad Beirami
Vahid Tarokh
47
27
0
19 Dec 2017
Spatial Mapping with Gaussian Processes and Nonstationary Fourier
  Features
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features
Jean-François Ton
Seth Flaxman
Dino Sejdinovic
Samir Bhatt
GP
73
55
0
15 Nov 2017
1