On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks

10 April 2017

Papers citing "On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks"

9 / 9 papers shown

Title
SLICK: Selective Localization and Instance Calibration for Knowledge-Enhanced Car Damage Segmentation in Automotive Insurance Teerapong Panboonyuen 153 0 0 12 Jun 2025
Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation Ainesh Bakshi Piotr Indyk Praneeth Kacham Sandeep Silwal Samson Zhou 104 4 0 01 Dec 2022
On The Computational Complexity of Self-Attention Feyza Duman Keles Pruthuvi Maheshakya Wijewardena Chinmay Hegde 142 130 0 11 Sep 2022
Symbolic Regression is NP-hard M. Virgolin S. Pissis 157 64 0 03 Jul 2022
Impossibility Results for Grammar-Compressed Linear Algebra Amir Abboud A. Backurs K. Bringmann Marvin Künnemann 26 12 0 27 Oct 2020
Fast Unbalanced Optimal Transport on a Tree Ryoma Sato M. Yamada H. Kashima OT 61 26 0 04 Jun 2020
Statistical and Computational Trade-Offs in Kernel K-Means Daniele Calandriello Lorenzo Rosasco 53 32 0 27 Aug 2019
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$ -means Clustering Manuel Fernández David P. Woodruff T. Yasuda 60 7 0 15 May 2019
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? Cameron Musco David P. Woodruff 105 13 0 05 Nov 2017