Diversity sampling is an implicit regularization for kernel methods

20 February 2020

Papers citing "Diversity sampling is an implicit regularization for kernel methods"

13 / 13 papers shown

Title
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging Luke Oakden-Rayner Jared A. Dunnmon G. Carneiro Christopher Ré OOD 55 380 0 27 Sep 2019
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices V. Chen Sen Wu Zhenzhen Weng Alexander Ratner Christopher Ré 50 56 0 13 Sep 2019
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling Nicholas Sterge Bharath K. Sriperumbudur Lorenzo Rosasco Alessandro Rudi 83 8 0 11 Jul 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 113 489 0 12 Jun 2019
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize Tengyuan Liang Alexander Rakhlin 44 353 0 01 Aug 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate M. Belkin Daniel J. Hsu P. Mitra AI4CE 122 256 0 13 Jun 2018
To understand deep learning we need to understand kernel learning M. Belkin Siyuan Ma Soumik Mandal 40 418 0 05 Feb 2018
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds Shusen Wang Alex Gittens Michael W. Mahoney 57 128 0 09 Jun 2017
Monte Carlo with Determinantal Point Processes Rémi Bardenet A. Hardy 35 79 0 02 May 2016
Fast DPP Sampling for Nyström with Application to Kernel Methods Chengtao Li Stefanie Jegelka S. Sra 33 76 0 19 Mar 2016
Less is More: Nyström Computational Regularization Alessandro Rudi Raffaello Camoriano Lorenzo Rosasco 31 277 0 16 Jul 2015
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 116 282 0 09 Aug 2012
Determinantal point processes for machine learning Alex Kulesza B. Taskar 213 1,130 0 25 Jul 2012