Joker: Joint Optimization Framework for Lightweight Kernel Machines

23 May 2025

Papers citing "Joker: Joint Optimization Framework for Lightweight Kernel Machines"

20 / 20 papers shown

Title
Fast training of large kernel models with delayed projections Amirhesam Abedsoltan Siyuan Ma Parthe Pandit Mikhail Belkin 92 1 0 25 Nov 2024
Stochastic Gradient Descent for Gaussian Processes Done Right J. Lin Shreyas Padhy Javier Antorán Austin Tripp Alexander Terenin Csaba Szepesvári José Miguel Hernández-Lobato David Janz 34 9 0 31 Oct 2023
Error Bounds for Learning with Vector-Valued Random Features S. Lanthaler Nicholas H. Nelsen 52 14 0 26 May 2023
Toward Large Kernel Models Amirhesam Abedsoltan M. Belkin Parthe Pandit 71 17 0 06 Feb 2023
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS Lin Chen Sheng Xu 112 94 0 22 Sep 2020
On the Similarity between the Laplace and Neural Tangent Kernels Amnon Geifman A. Yadav Yoni Kasten Meirav Galun David Jacobs Ronen Basri 99 93 0 03 Jul 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 61 115 0 18 Jun 2020
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses Ulysse Marteau-Ferey Francis R. Bach Alessandro Rudi 45 36 0 03 Jul 2019
On Fast Leverage Score Sampling and Optimal Learning Alessandro Rudi Daniele Calandriello Luigi Carratino Lorenzo Rosasco 43 81 0 31 Oct 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 77 1,088 0 28 Sep 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 206 3,160 0 20 Jun 2018
FALKON: An Optimal Large Scale Kernel Method Alessandro Rudi Luigi Carratino Lorenzo Rosasco 44 196 0 31 May 2017
Diving into the shallows: a computational perspective on large-scale shallow learning Siyuan Ma M. Belkin 53 78 0 30 Mar 2017
GPflow: A Gaussian process library using TensorFlow A. G. Matthews Mark van der Wilk T. Nickson Keisuke Fujii A. Boukouvalas Pablo León-Villagrá Zoubin Ghahramani J. Hensman GP 63 662 0 27 Oct 2016
Large Scale Kernel Learning using Block Coordinate Descent Stephen Tu Rebecca Roelofs Shivaram Venkataraman Benjamin Recht 38 42 0 17 Feb 2016
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection J. Nutini Mark Schmidt I. Laradji M. Friedlander H. Koepke 66 222 0 01 Jun 2015
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization Zheng Qu Peter Richtárik Martin Takáč Olivier Fercoq ODL 72 99 0 08 Feb 2015
Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai Bo Xie Niao He Yingyu Liang Anant Raj Maria-Florina Balcan Le Song 109 230 0 21 Jul 2014
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 121 1,031 0 10 Sep 2012
Iteration Complexity of Randomized Block-Coordinate Descent Methods for Minimizing a Composite Function Peter Richtárik Martin Takáč 86 769 0 14 Jul 2011