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Dataset Distillation is the task of synthesizing small datasets from large ones while still retaining comparable predictive accuracy to the original uncompressed dataset.
Sparse gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2005
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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A unified framework for approximating and clustering data
Dan Feldman and Michael Langberg · 2011
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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On the error of random fourier features
Danica J Sutherland and Jeff Schneider · 2015
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New frameworks for offline and streaming coreset constructions
Vladimir Braverman, Dan Feldman, Harry Lang, Adiel Statman, and Samson Zhou · 2016
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Approximate k-means++ in sublinear time
Olivier Bachem, Mario Lucic, S. Hamed Hassani, and Andreas Krause · 2016
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Coresets for scalable bayesian logistic regression
Jonathan H. Huggins, Trevor Campbell, and Tamara Broderick · 2016
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Strong coresets for hard and soft bregman clustering with applications to exponential family mixtures
Mario Lucic, Olivier Bachem, and Andreas Krause · 2016
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The variational gaussian process, 2016
Dustin Tran, Rajesh Ranganath, and David M. Blei · 2016
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Random fourier features for kernel ridge regression: Approximation bounds and statistical guarantees
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, and Amir Zandieh · 2017
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Generalization properties of learning with random features
Alessandro Rudi and Lorenzo Rosasco · 2017
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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On coresets for logistic regression
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, and David Woodruff · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Rates of convergence for sparse variational Gaussian process regression
David Burt, Carl Edward Rasmussen, and Mark Van Der Wilk · 2019
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Introduction to coresets: Accurate coresets
Ibrahim Jubran, Alaa Maalouf, and Dan Feldman · 2019
Cited alongside, same era.
Towards a unified analysis of random fourier features
Zhu Li, Jean-Francois Ton, Dino Oglic, and Dino Sejdinovic · 2019
Cited alongside, same era.
Fast and accurate least-mean-squares solvers
Alaa Maalouf, Ibrahim Jubran, and Dan Feldman · 2019
Cited alongside, same era.
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2019
Cited alongside, same era.
Coresets via bilevel optimization for continual learning and streaming
Zalán Borsos, Mojmir Mutny, and Andreas Krause · 2020
Cited alongside, same era.
On coresets for support vector machines
Murad Tukan, Cenk Baykal, Dan Feldman, and Daniela Rus · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Private set generation with discriminative information
Dingfan Chen, Raouf Kerkouche, and Mario Fritz · 2022
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
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Efficient dataset distillation using random feature approximation
Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus · 2022
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Implicit regularization of random feature models
Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clément Hongler, and Franck Gabriel · 2020
Cited alongside, same era.
Sets clustering
Ibrahim Jubran, Murad Tukan, Alaa Maalouf, and Dan Feldman · 2020
Cited alongside, same era.
Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff A. Bilmes, and Jure Leskovec · 2020
Cited alongside, same era.
Tight sensitivity bounds for smaller coresets
Alaa Maalouf, Adiel Statman, and Dan Feldman · 2020
Cited alongside, same era.
Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2020
Cited alongside, same era.
Coresets for near-convex functions
Murad Tukan, Alaa Maalouf, and Dan Feldman · 2020
Cited alongside, same era.
Towards a unified analysis of random fourier features
Zhu Li, Jean-Francois Ton, Dino Oglic, and Dino Sejdinovic · 2021
Cited alongside, same era.
Later among the works it cites.
Evolution of neural tangent kernels under benign and adversarial training
Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus · 2022
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A unified approach to coreset learning
Alaa Maalouf, Gilad Eini, Ben Mussay, Dan Feldman, and Margarita Osadchy · 2022
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Coresets for data discretization and sine wave fitting
Alaa Maalouf, Murad Tukan, Eric Price, Daniel M Kane, and Dan Feldman · 2022
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Adaptive second order coresets for data-efficient machine learning
Omead Pooladzandi, David Davini, and Baharan Mirzasoleiman · 2022
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Sample condensation in online continual learning, 2022
Mattia Sangermano, Antonio Carta, Andrea Cossu, and Davide Bacciu · 2022
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New coresets for projective clustering and applications
Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, and Dan Feldman · 2022
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Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 2022
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Dataset distillation with convexified implicit gradients, 2023
Noel Loo, Ramin Hasani, Mathias Lechner, and Daniela Rus · 2023
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Provable data subset selection for efficient neural network training
Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, and Dan Feldman · 2023
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