Generalization Properties of Learning with Random Features

14 February 2016

Papers citing "Generalization Properties of Learning with Random Features"

50 / 62 papers shown

Title
Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation Ninh Pham Rasmus Pagh 27 0 0 13 May 2025
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime Francesco Camilli D. Tieplova Eleonora Bergamin Jean Barbier 109 0 0 06 May 2025
Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices Chanwoo Chun SueYeon Chung Daniel D. Lee 26 1 0 23 Oct 2024
Optimal Kernel Quantile Learning with Random Features Caixing Wang Xingdong Feng 42 0 0 24 Aug 2024
Deep Learning without Global Optimization by Random Fourier Neural Networks Owen Davis Gianluca Geraci Mohammad Motamed BDL 54 0 0 16 Jul 2024
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features Oliver R. A. Dunbar Nicholas H. Nelsen Maya Mutic 25 5 0 30 Jun 2024
Universal randomised signatures for generative time series modelling Francesca Biagini Lukas Gonon Niklas Walter 40 4 0 14 Jun 2024
Scaling Laws in Linear Regression: Compute, Parameters, and Data Licong Lin Jingfeng Wu Sham Kakade Peter L. Bartlett Jason D. Lee LRM 38 15 0 12 Jun 2024
Overcoming Saturation in Density Ratio Estimation by Iterated Regularization Lukas Gruber Markus Holzleitner Johannes Lehner Sepp Hochreiter Werner Zellinger 46 1 0 21 Feb 2024
Random features models: a way to study the success of naive imputation Alexis Ayme Claire Boyer Lpsm Aymeric Dieuleveut Erwan Scornet 22 3 0 06 Feb 2024
Potential and limitations of random Fourier features for dequantizing quantum machine learning R. Sweke Erik Recio Sofiene Jerbi Elies Gil-Fuster Bryce Fuller Jens Eisert Johannes Jakob Meyer 22 12 0 20 Sep 2023
Error Bounds for Learning with Vector-Valued Random Features S. Lanthaler Nicholas H. Nelsen 27 12 0 26 May 2023
When is Importance Weighting Correction Needed for Covariate Shift Adaptation? Davit Gogolashvili Matteo Zecchin Motonobu Kanagawa Marios Kountouris Maurizio Filippone 30 6 0 07 Mar 2023
A Distribution Free Truncated Kernel Ridge Regression Estimator and Related Spectral Analyses Asma Ben Saber Abderrazek Karoui 11 1 0 17 Jan 2023
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks Ilja Kuzborskij Csaba Szepesvári 21 4 0 28 Dec 2022
Vector-Valued Least-Squares Regression under Output Regularity Assumptions Luc Brogat-Motte Alessandro Rudi Céline Brouard Juho Rousu Florence dÁlché-Buc 18 6 0 16 Nov 2022
Unbalanced Optimal Transport, from Theory to Numerics Thibault Séjourné Gabriel Peyré Franccois-Xavier Vialard OT 25 47 0 16 Nov 2022
SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics Esha Saha L. Ho Giang Tran 36 5 0 11 Nov 2022
RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features Mateus P. Otto Rafael Izbicki 27 1 0 11 Nov 2022
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 164 67 0 27 Oct 2022
Importance Weighting Correction of Regularized Least-Squares for Covariate and Target Shifts Davit Gogolashvili OOD 18 1 0 18 Oct 2022
On The Relative Error of Random Fourier Features for Preserving Kernel Distance Kuan Cheng S. Jiang Luojian Wei Zhide Wei 36 1 0 01 Oct 2022
Fast Kernel Methods for Generic Lipschitz Losses via $p$ -Sparsified Sketches T. Ahmad Pierre Laforgue Florence dÁlché-Buc 19 5 0 08 Jun 2022
Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable Promit Ghosal Srinath Mahankali Yihang Sun MLT 19 4 0 24 May 2022
Concentration of Random Feature Matrices in High-Dimensions Zhijun Chen Hayden Schaeffer Rachel A. Ward 20 6 0 14 Apr 2022
SRMD: Sparse Random Mode Decomposition Nicholas Richardson Hayden Schaeffer Giang Tran 21 11 0 12 Apr 2022
Information Theory with Kernel Methods Francis R. Bach 23 40 0 17 Feb 2022
HARFE: Hard-Ridge Random Feature Expansion Esha Saha Hayden Schaeffer Giang Tran 38 14 0 06 Feb 2022
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings M. Scetbon Laurent Meunier Yaniv Romano 25 10 0 28 Oct 2021
Conditioning of Random Feature Matrices: Double Descent and Generalization Error Zhijun Chen Hayden Schaeffer 35 12 0 21 Oct 2021
Sampling from Arbitrary Functions via PSD Models Ulysse Marteau-Ferey Francis R. Bach Alessandro Rudi 14 10 0 20 Oct 2021
Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality Lukas Gonon 11 35 0 14 Jun 2021
Statistical Optimality and Computational Efficiency of Nyström Kernel PCA Nicholas Sterge Bharath K. Sriperumbudur 25 8 0 19 May 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 46 89 0 25 Feb 2021
Denoising Score Matching with Random Fourier Features Olga Tsymboi Yermek Kapushev Evgeny Burnaev Ivan V. Oseledets 31 1 0 13 Jan 2021
Probabilistic Load Forecasting Based on Adaptive Online Learning Verónica Álvarez Santiago Mazuelas Jose A. Lozano 11 61 0 30 Nov 2020
Deep Equals Shallow for ReLU Networks in Kernel Regimes A. Bietti Francis R. Bach 23 86 0 30 Sep 2020
Multiple Descent: Design Your Own Generalization Curve Lin Chen Yifei Min M. Belkin Amin Karbasi DRL 18 61 0 03 Aug 2020
When Does Preconditioning Help or Hurt Generalization? S. Amari Jimmy Ba Roger C. Grosse Xuechen Li Atsushi Nitanda Taiji Suzuki Denny Wu Ji Xu 34 32 0 18 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 34 172 0 23 Apr 2020
Deep Randomized Neural Networks Claudio Gallicchio Simone Scardapane OOD 36 61 0 27 Feb 2020
Implicit Regularization of Random Feature Models Arthur Jacot Berfin Simsek Francesco Spadaro Clément Hongler Franck Gabriel 18 82 0 19 Feb 2020
COKE: Communication-Censored Decentralized Kernel Learning Ping Xu Yue Wang Xiang Chen Z. Tian 11 20 0 28 Jan 2020
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping Shusen Wang 23 2 0 24 Sep 2019
Graph Random Neural Features for Distance-Preserving Graph Representations Daniele Zambon C. Alippi L. Livi 16 1 0 09 Sep 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 39 626 0 14 Aug 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 13 241 0 27 Apr 2019
Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional Data Khalil Elkhalil A. Kammoun Xiangliang Zhang Mohamed-Slim Alouini Tareq Al-Naffouri 11 7 0 19 Apr 2019
Spatial Analysis Made Easy with Linear Regression and Kernels Philip Milton E. Giorgi Samir Bhatt 19 16 0 22 Feb 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 25 1,610 0 28 Dec 2018