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We give relative error coresets for training linear classifiers with a broad class of loss functions, including the logistic loss and hinge loss.
On coresets for k-means and k-median clustering
Sariel Har-Peled and Soham Mazumdar · 2004
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Geometric approximation via coresets
Pankaj K Agarwal, Sariel Har-Peled, and Kasturi R Varadarajan · 2005
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Sampling algorithms for l 2 l_{2} regression and applications
Petros Drineas, Michael W Mahoney, and Shan Muthukrishnan · 2006
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Sampling algorithms and coresets for ℓ p \ell_{p} regression
Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, and Michael W Mahoney · 2009
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A unified framework for approximating and clustering data
Dan Feldman and Michael Langberg · 2011
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Sketching for M-estimators: A unified approach to robust regression
Kenneth L Clarkson and David P Woodruff · 2014
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Dimensionality reduction for k-means clustering and low rank approximation
Michael B Cohen, Sam Elder, Cameron Musco, Christopher Musco, and Madalina Persu · 2015
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Uniform sampling for matrix approximation
Michael B Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, and Aaron Sidford · 2015
Earlier work this paper cites.
ℓ p \ell_{p} row sampling by Lewis weights
Michael B Cohen and Richard Peng · 2015
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Communication complexity (for algorithm designers)
Tim Roughgarden · 2015
Earlier work this paper cites.
Coresets for scalable Bayesian logistic regression
Jonathan H Huggins, Trevor Campbell, and Tamara Broderick · 2016
Cited alongside, same era.
Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
Cited alongside, same era.
Practical coreset constructions for machine learning
Olivier Bachem, Mario Lucic, and Andreas Krause · 2017
Cited alongside, same era.
Subspace embedding and linear regression with Orlicz norm
Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, and Ruiqi Zhong · 2018
Cited alongside, same era.
On coresets for logistic regression
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, and David P Woodruff · 2018
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
Dimensionality reduction for Tukey regression
Kenneth Clarkson, Ruosong Wang, and David Woodruff · 2019
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Discrepancy, coresets, and sketches in machine learning
Zohar Karnin and Edo Liberty · 2019
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Near optimal linear algebra in the online and sliding window models
Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P Woodruff, and Samson Zhou · 2020
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Data-independent neural pruning via coresets
Mussay Ben, Margarita Osadchy, Vladimir Braverman, Samson Zhou, and Dan Feldman · 2020
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Turning big data into tiny data: Constant-size coresets for k-means, PCA, and projective clustering
Dan Feldman, Melanie Schmidt, and Christian Sohler · 2020
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Near-optimal coresets of kernel density estimates
Jeff M Phillips and Wai Ming Tai · 2020
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Cited alongside, same era.
Optimal subsampling with influence functions
Daniel Ting and Eric Brochu · 2018
Cited alongside, same era.
Generic coreset for scalable learning of monotonic kernels: Logistic regression, sigmoid and more
Elad Tolochinsky and Dan Feldman · 2018
Cited alongside, same era.
Optimal subsampling for large sample logistic regression
HaiYing Wang, Rong Zhu, and Ping Ma · 2018
Cited alongside, same era.
On coresets for regularized loss minimization
Ryan R Curtin, Sungjin Im, Ben Moseley, Kirk Pruhs, and Alireza Samadian · 2019
Cited alongside, same era.
Probability bounds
John C. Duchi
Cited in the paper.
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Query complexity of least absolute deviation regression via robust uniform convergence
Xue Chen and Michał Dereziński · 2021
Closest in time.
Subspace embeddings under nonlinear transformations
Aarshvi Gajjar and Cameron Musco · 2021
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Oblivious sketching for logistic regression
Alexander Munteanu, Simon Omlor, and David Woodruff · 2021
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