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Comparing Distributions and Shapes using the Kernel Distance
v1v2 (latest)

Comparing Distributions and Shapes using the Kernel Distance

International Symposium on Computational Geometry (SoCG), 2010
4 January 2010
S. Joshi
Raj Varma Kommaraju
J. M. Phillips
Suresh Venkatasubramanian
ArXiv (abs)PDFHTML

Papers citing "Comparing Distributions and Shapes using the Kernel Distance"

23 / 23 papers shown
Contextualized Messages Boost Graph Representations
Contextualized Messages Boost Graph Representations
Brian Godwin Lim
Galvin Brice Lim
Renzo Roel Tan
Kazushi Ikeda
AI4CE
655
5
0
19 Mar 2024
No Dimensional Sampling Coresets for Classification
No Dimensional Sampling Coresets for Classification
M. Alishahi
Jeff M. Phillips
382
4
0
07 Feb 2024
Stronger Coreset Bounds for Kernel Density Estimators via Chaining
Stronger Coreset Bounds for Kernel Density Estimators via Chaining
Rainie Bozzai
T. Rothvoss
215
0
0
12 Oct 2023
Mixed-type Distance Shrinkage and Selection for Clustering via Kernel
  Metric Learning
Mixed-type Distance Shrinkage and Selection for Clustering via Kernel Metric LearningJournal of Classification (J. Classif.), 2023
Jesse S. Ghashti
John R.J. Thompson
239
7
0
02 Jun 2023
KDEformer: Accelerating Transformers via Kernel Density Estimation
KDEformer: Accelerating Transformers via Kernel Density EstimationInternational Conference on Machine Learning (ICML), 2023
A. Zandieh
Insu Han
Majid Daliri
Amin Karbasi
520
55
0
05 Feb 2023
Kernel Thinning
Kernel ThinningAnnual Conference Computational Learning Theory (COLT), 2021
Raaz Dwivedi
Lester W. Mackey
980
47
0
12 May 2021
Introduction to Core-sets: an Updated Survey
Introduction to Core-sets: an Updated Survey
Dan Feldman
382
76
0
18 Nov 2020
A Statistical Perspective on Coreset Density Estimation
A Statistical Perspective on Coreset Density Estimation
Paxton Turner
Jingbo Liu
Philippe Rigollet
308
10
0
10 Nov 2020
Optimal Coreset for Gaussian Kernel Density Estimation
Optimal Coreset for Gaussian Kernel Density EstimationInternational Symposium on Computational Geometry (SoCG), 2020
W. Tai
383
11
0
15 Jul 2020
The GaussianSketch for Almost Relative Error Kernel Distance
The GaussianSketch for Almost Relative Error Kernel Distance
J. M. Phillips
W. Tai
270
1
0
09 Nov 2018
Simple Distances for Trajectories via Landmarks
Simple Distances for Trajectories via Landmarks
J. M. Phillips
Pingfan Tang
190
6
0
30 Apr 2018
Near-Optimal Coresets of Kernel Density Estimates
Near-Optimal Coresets of Kernel Density Estimates
J. M. Phillips
W. Tai
450
80
0
06 Feb 2018
Improved Coresets for Kernel Density Estimates
Improved Coresets for Kernel Density Estimates
J. M. Phillips
W. Tai
275
45
0
11 Oct 2017
Visualization of Big Spatial Data using Coresets for Kernel Density
  Estimates
Visualization of Big Spatial Data using Coresets for Kernel Density Estimates
Yan Zheng
Y. Ou
A. Lex
J. M. Phillips
116
28
0
13 Sep 2017
Coresets for Kernel Regression
Coresets for Kernel RegressionKnowledge Discovery and Data Mining (KDD), 2017
Yan Zheng
J. M. Phillips
307
21
0
13 Feb 2017
Relative Error Embeddings for the Gaussian Kernel Distance
Relative Error Embeddings for the Gaussian Kernel Distance
Di Chen
J. M. Phillips
308
15
0
17 Feb 2016
Subsampling in Smoothed Range Spaces
Subsampling in Smoothed Range Spaces
J. M. Phillips
Yan Zheng
184
4
0
30 Oct 2015
Sparse Approximation of a Kernel Mean
Sparse Approximation of a Kernel Mean
Efrén Cruz Cortés
C. Scott
209
20
0
01 Mar 2015
Confidence sets for persistence diagrams
Confidence sets for persistence diagramsAnnals of Statistics (AoS), 2013
Brittany Terese Fasy
F. Lecci
Alessandro Rinaldo
Larry A. Wasserman
Sivaraman Balakrishnan
Aarti Singh
465
295
0
28 Mar 2013
Hierarchical Graphical Models for Multigroup Shape Analysis using
  Expectation Maximization with Sampling in Kendall's Shape Space
Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space
Yen-Yun Yu
P. T. Fletcher
Suyash P. Awate
176
1
0
22 Dec 2012
epsilon-Samples of Kernels
epsilon-Samples of KernelsACM-SIAM Symposium on Discrete Algorithms (SODA), 2011
J. M. Phillips
355
49
0
18 Dec 2011
Generating a Diverse Set of High-Quality Clusterings
Generating a Diverse Set of High-Quality Clusterings
J. M. Phillips
Parasaran Raman
Suresh Venkatasubramanian
224
12
0
29 Jul 2011
A Gentle Introduction to the Kernel Distance
A Gentle Introduction to the Kernel Distance
J. M. Phillips
Suresh Venkatasubramanian
482
91
0
08 Mar 2011
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