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Kernel matrices, as well as weighted graphs represented by them, are ubiquitous objects in machine learning, statistics and other related fields.
A fast parametric maximum flow algorithm and applications
Giorgio Gallo, Michael D. Grigoriadis, and Robert Endre Tarjan · 1989
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Lower bounds for the first eigenvalue of certain m-matrices associated with graphs
Shmuel Friedland · 1992
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Laplacian matrices of graphs: a survey
Russell Merris · 1994
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Spectral graph theory
Fan RK Chung and Fan Chung Graham · 1997
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The mnist database of handwritten digits
Yann LeCun · 1998
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Greedy approximation algorithms for finding dense components in a graph
Moses Charikar · 2000
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N-body problems in statistical learning
Alexander G Gray and Andrew W Moore · 2001
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On the complexity of k-sat
Russell Impagliazzo and Ramamohan Paturi · 2001
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On Cheeger-type inequalities for weighted graphs
Shmuel Friedland and Reinhard Nabben · 2002
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Network motifs: Simple building blocks of complex networks
R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon · 2002
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Bernhard Schölkopf, Alexander J Smola, Francis Bach, et al · 2002
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Nonparametric density estimation: Toward computational tractability
Alexander G Gray and Andrew W Moore · 2003
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Fast monte-carlo algorithms for finding low-rank approximations
Alan Frieze, Ravi Kannan, and Santosh Vempala · 2004
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Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems
Daniel A. Spielman and Shang-Hua Teng · 2004
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Kernel methods for pattern analysis
John Shawe-Taylor, Nello Cristianini, et al · 2004
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Clustering coefficients for weighted networks
Gabriela Kalna and Desmond J Higham · 2006
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Dual-tree fast gauss transforms
Dongryeol Lee, Andrew W Moore, and Alexander G Gray · 2006
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The structure of weighted small-world networks
Wenyuan Li, Yongjing Lin, and Ying Liu · 2007
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A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
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Size bounds and query plans for relational joins
Albert Atserias, Martin Grohe, and Daniel Marx · 2008
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Statistical analysis of weighted networks
Ioannis E Antoniou and ET Tsompa · 2008
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Kernel methods in machine learning
Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola · 2008
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Testing expansion in bounded degree graphs
Satyen Kale and C Seshadhri · 2008
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Microscopic evolution of social networks
Jure Leskovec, Lars Backstrom, Ravi Kumar, and Andrew Tomkins · 2008
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Fast high-dimensional kernel summations using the monte carlo multipole method
Dongryeol Lee and Alexander Gray · 2008
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Automatic online tuning for fast gaussian summation
Vlad I Morariu, Balaji Vasan Srinivasan, Vikas C Raykar, Ramani Duraiswami, and Larry S Davis · 2008
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Linear-time algorithms for pairwise statistical problems
Parikshit Ram, Dongryeol Lee, William March, and Alexander Gray · 2009
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Testing expansion in bounded-degree graphs
Artur Czumaj and Christian Sohler · 2010
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Is a ”friend” a friend? investigating the structure of friendship networks in virtual worlds
Brooke Foucault Welles, Anne Van Devender, and Noshir Contractor · 2010
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Efficient triangle counting in large graphs via degree-based vertex partitioning
Mihail N. Kolountzakis, Gary L. Miller, Richard Peng, and Charalampos E. Tsourakakis · 2010
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A survey of algorithms for dense subgraph discovery
Victor E. Lee, Ning Ruan, Ruoming Jin, and Charu C. Aggarwal · 2010
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On testing expansion in bounded-degree graphs
Oded Goldreich and Dana Ron · 2011
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A nearly-m log n time solver for sdd linear systems
Ioannis Koutis, Gary L. Miller, and Richard Peng · 2011
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Graph sparsification by effective resistances
Daniel A Spielman and Nikhil Srivastava · 2011
Introduction to property testing
Oded Goldreich · 2017
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Sublinear time low-rank approximation of positive semidefinite matrices
Cameron Musco and David P Woodruff · 2017
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Triangle counting in large networks: a review
Mohammad Al Hasan and Vachik S Dave · 2018
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Efficient density evaluation for smooth kernels
Arturs Backurs, Moses Charikar, Piotr Indyk, and Paris Siminelakis · 2018
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Sublinear time low-rank approximation of distance matrices
Ainesh Bakshi and David Woodruff · 2018
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Testing graph clusterability: Algorithms and lower bounds
Ashish Chiplunkar, Michael Kapralov, Sanjeev Khanna, Aida Mousavifar, and Yuval Peres · 2018
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Multi-way spectral partitioning and higher-order cheeger inequalities
James R. Lee, Shayan Oveis Gharan, and Luca Trevisan · 2012
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Many sparse cuts via higher eigenvalues
Anand Louis, Prasad Raghavendra, Prasad Tetali, and Santosh S. Vempala · 2012
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Spectral sparsification of graphs: theory and algorithms
Joshua Batson, Daniel A Spielman, Nikhil Srivastava, and Shang-Hua Teng · 2013
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Low rank approximation and regression in input sparsity time
Kenneth L. Clarkson and David P. Woodruff · 2013
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Improved cheeger’s inequality: analysis of spectral partitioning algorithms through higher order spectral gap
Tsz Chiu Kwok, Lap Chi Lau, Yin Tat Lee, Shayan Oveis Gharan, and Luca Trevisan · 2013
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ε \varepsilon -samples for kernels
Jeff M Phillips · 2013
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Approximating the spectrum of a graph
David Cohen-Steiner, Weihao Kong, Christian Sohler, and Gregory Valiant · 2018
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Constructing linear-sized spectral sparsification in almost-linear time
Yin Tat Lee and He Sun · 2018
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Improved coresets for kernel density estimates
Jeff M. Phillips and Wai Ming Tai · 2018
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Space and time efficient kernel density estimation in high dimensions
Arturs Backurs, Piotr Indyk, and Tal Wagner · 2019
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Spectral concentration and greedy k-clustering
Tamal K Dey, Pan Peng, Alfred Rossi, and Anastasios Sidiropoulos · 2019
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Sample-optimal low-rank approximation of distance matrices
Piotr Indyk, Ali Vakilian, Tal Wagner, and David P. Woodruff · 2019
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Rehashing kernel evaluation in high dimensions
Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, and Philip Alexander Levis · 2019
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Algorithms and hardness for linear algebra on geometric graphs
Josh Alman, Timothy Chu, Aaron Schild, and Zhao Song · 2020
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Testing positive semi-definiteness via random submatrices
Ainesh Bakshi, Nadiia Chepurko, and Rajesh Jayaram · 2020
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Robust and sample optimal algorithms for psd low rank approximation
Ainesh Bakshi, Nadiia Chepurko, and David P Woodruff · 2020
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Kernel density estimation through density constrained near neighbor search
Moses Charikar, Michael Kapralov, Navid Nouri, and Paris Siminelakis · 2020
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Robust clustering oracle and local reconstructor of cluster structure of graphs
Pan Peng · 2020
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Near-optimal coresets of kernel density estimates
Jeff M. Phillips and Wai Ming Tai · 2020
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Near-optimal coresets of kernel density estimates
Jeff M Phillips and Wai Ming Tai · 2020
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Faster kernel matrix algebra via density estimation
Arturs Backurs, Piotr Indyk, Cameron Musco, and Tal Wagner · 2021
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Sublinear time eigenvalue approximation via random sampling
Rajarshi Bhattacharjee, Cameron Musco, and Archan Ray · 2021
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Spectral clustering oracles in sublinear time
Grzegorz Gluch, Michael Kapralov, Silvio Lattanzi, Aida Mousavifar, and Christian Sohler · 2021
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Spectral sparsification of metrics and kernels
Kent Quanrud · 2021
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Triangle and four cycle counting with predictions in graph streams
Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, and Michael Zhang · 2022
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Deann: Speeding up kernel-density estimation using approximate nearest neighbor search
Matti Karppa, Martin Aumüller, and Rasmus Pagh · 2022
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Optimal coreset for gaussian kernel density estimation
Wai Ming Tai · 2022
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