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Sketching is one of the most fundamental tools in large-scale machine learning.
Über dyadische brüche
Aleksandr Khintchine · 1923
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On a modification of chebyshev’s inequality and of the error formula of laplace
Sergei Bernstein · 1924
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A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations
Herman Chernoff · 1952
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Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1963
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A bound on tail probabilities for quadratic forms in independent random variables
David Lee Hanson and Farroll Tim Wright · 1971
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The best constants in the khintchine inequality
Uffe Haagerup · 1981
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The space complexity of approximating the frequency moments
Noga Alon, Yossi Matias, and Mario Szegedy · 1999
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Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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Finding frequent items in data streams
Moses Charikar, Kevin Chen, and Martin Farach-Colton · 2002
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Improved approximation algorithms for large matrices via random projections
Tamás Sarlós · 2006
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan · 2010
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An introduction to heavy-tailed and subexponential distributions
Sergey Foss, Dmitry Korshunov, and Stan Zachary · 2011
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Improved analysis of the subsampled randomized hadamard transform
Joel A Tropp · 2011
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
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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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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2013
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Privacy via the johnson-lindenstrauss transform
Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra · 2013
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Faster ridge regression via the subsampled randomized hadamard transform
Yichao Lu, Paramveer Dhillon, Dean P Foster, and Lyle Ungar · 2013
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Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
Xiangrui Meng and Michael W Mahoney · 2013
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Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings
Jelani Nelson and Huy L Nguyên · 2013
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Hanson-wright inequality and sub-gaussian concentration
Mark Rudelson and Roman Vershynin · 2013
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Optimal cur matrix decompositions
Christos Boutsidis and David P Woodruff · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Sketching as a tool for numerical linear algebra
David P. Woodruff · 2014
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Giuseppe Ateniese, Luigi V Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici · 2015
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Differentially private release and learning of threshold functions
M. Bun, K. Nissim, U. Stemmer, and S. Vadhan · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Cited alongside, same era.
Optimal principal component analysis in distributed and streaming models
Christos Boutsidis, David P Woodruff, and Peilin Zhong · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Cited alongside, same era.
Defeating image obfuscation with deep learning
Richard McPherson, Reza Shokri, and Vitaly Shmatikov · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Cited alongside, same era.
Distributed low rank approximation of implicit functions of a matrix
A survey of privacy attacks in machine learning
Maria Rigaki and Sebastián García · 2020
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Fetchsgd: Communication-efficient federated learning with sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora · 2020
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A framework for evaluating gradient leakage attacks in federated learning
Wenqi Wei, Ling Liu, M. Loper, Ka-Ho Chow, M. Gursoy, Stacey Truex, and Yanzhao Wu · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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Planning with general objective functions: Going beyond total rewards
Ruosong Wang, Peilin Zhong, Simon S Du, Russ R Salakhutdinov, and Lin F Yang · 2020
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David P Woodruff and Peilin Zhong · 2016
Cited alongside, same era.
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Cited alongside, same era.
Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Low rank approximation with entrywise ℓ 1 \ell_{1} -norm error
Zhao Song, David P Woodruff, and Peilin Zhong · 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.
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Federated meta-learning for fraudulent credit card detection
Wenbo Zheng, Lan Yan, Chao Gou, and Fei-Yue Wang · 2020
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Training (over-parametrized) neural networks in near-linear time
Jan van den Brand, Binghui Peng, Zhao Song, and Omri Weinstein · 2021
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Almost linear time density level set estimation via dbscan
Hossein Esfandiari, Vahab Mirrokni, and Peilin Zhong · 2021
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Faster dynamic matrix inverse for faster lps
Shunhua Jiang, Zhao Song, Omri Weinstein, and Hengjie Zhang · 2021
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FedBN: Federated learning on non-IID features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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Oblivious sketching-based central path method for solving linear programming problems
Zhao Song and Zheng Yu · 2021
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Does preprocessing help training over-parameterized neural networks?
Zhao Song, Shuo Yang, and Ruizhe Zhang · 2021
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Training multi-layer over-parametrized neural network in subquadratic time
Zhao Song, Lichen Zhang, and Ruizhe Zhang · 2021
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Breaking the linear iteration cost barrier for some well-known conditional gradient methods using maxip data-structures
Zhaozhuo Xu, Zhao Song, and Anshumali Shrivastava · 2021
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See through gradients: Image batch recovery via gradinversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M Alvarez, Jan Kautz, and Pavlo Molchanov · 2021
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A nearly optimal size coreset algorithm with nearly linear time
Yichuan Deng, Zhao Song, Yitan Wang, and Yuanyuan Yang · 2022
Closest in time.
A sublinear adversarial training algorithm
Yeqi Gao, Lianke Qin, Zhao Song, and Yitan Wang · 2022
Closest in time.
Adore: Differentially oblivious relational database operators
Lianke Qin, Rajesh Jayaram, Elaine Shi, Zhao Song, Danyang Zhuo, and Shumo Chu · 2022
Closest in time.
Adaptive and dynamic multi-resolution hashing for pairwise summations
Lianke Qin, Aravind Reddy, Zhao Song, Zhaozhuo Xu, and Danyang Zhuo · 2022
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Online map inference and learning for nonsymmetric determinantal point processes
Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, and Nesreen Ahmed · 2022
Closest in time.
Dynamic tensor product regression
Aravind Reddy, Zhao Song, and Lichen Zhang · 2022
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Federated adversarial learning: A framework with convergence analysis
Xiaoxiao Li, Zhao Song, and Jiaming Yang · 2023
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Private query release via the johnson-lindenstrauss transform
Aleksandar Nikolov · 2023
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Fast submodular function maximization
Lianke Qin, Zhao Song, and Yitan Wang · 2023
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A general algorithm for solving rank-one matrix sensing
Lianke Qin, Zhao Song, and Ruizhe Zhang · 2023
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An online and unified algorithm for projection matrix vector multiplication with application to empirical risk minimization
Lianke Qin, Zhao Song, Lichen Zhang, and Danyang Zhuo · 2023
Closest in time.
A tale of two efficient value iteration algorithms for solving linear mdps with large action space
Anshumali Shrivastava, Zhao Song, and Zhaozhuo Xu · 2023
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Reconstructing training data from model gradient, provably
Zihan Wang, Jason Lee, and Qi Lei · 2023
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