Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimal

14 April 2020

Papers citing "Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimal"

6 / 6 papers shown

Title
No Dimensional Sampling Coresets for Classification M. Alishahi Jeff M. Phillips 39 1 0 07 Feb 2024
Parameterized Approximation Schemes for Clustering with General Norm Objectives F. Abbasi Sandip Banerjee J. Byrka Parinya Chalermsook Ameet Gadekar K. Khodamoradi D. Marx Roohani Sharma J. Spoerhase 23 12 0 06 Apr 2023
Provable Data Subset Selection For Efficient Neural Network Training M. Tukan Samson Zhou Alaa Maalouf Daniela Rus Vladimir Braverman Dan Feldman MLT 25 9 0 09 Mar 2023
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$ -Means Clustering Lingxiao Huang Zhize Li Jialin Sun Haoyu Zhao FedML 41 9 0 26 Oct 2022
Efficient NTK using Dimensionality Reduction Nir Ailon Supratim Shit 26 0 0 10 Oct 2022
Adversarial Robustness of Streaming Algorithms through Importance Sampling Vladimir Braverman Avinatan Hassidim Yossi Matias Mariano Schain Sandeep Silwal Samson Zhou AAML OOD 24 38 0 28 Jun 2021