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Projection maintenance is one of the core data structure tasks.
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 new algorithm for minimizing convex functions over convex sets
Pravin M Vaidya · 1989
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Speeding-up linear programming using fast matrix multiplication
Pravin M Vaidya · 1989
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Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
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The space complexity of approximating the frequency moments
Noga Alon, Yossi Matias, and Mario Szegedy · 1999
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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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Differential privacy
Cynthia Dwork · 2006
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2008
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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Probabilistic inference and differential privacy
Oliver Williams and Frank McSherry · 2010
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Efficient sketches for the set query problem
Eric Price · 2011
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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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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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Optimal cur matrix decompositions
Christos Boutsidis and David P Woodruff · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil Vadhan · 2015
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
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Unifying and strengthening hardness for dynamic problems via the online matrix-vector multiplication conjecture
Monika Henzinger, Sebastian Krinninger, Danupon Nanongkai, and Thatchaphol Saranurak · 2015
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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Optimal principal component analysis in distributed and streaming models
Christos Boutsidis, David P Woodruff, and Peilin Zhong · 2016
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Simultaneous diagonalization of matrices and its applications in quadratically constrained quadratic programming
Rujun Jiang and Duan Li · 2016
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Weighted low rank approximations with provable guarantees
Ilya Razenshteyn, Zhao Song, and David P Woodruff · 2016
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Distributed low rank approximation of implicit functions of a matrix
David P Woodruff and Peilin Zhong · 2016
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Faster online matrix-vector multiplication
Kasper Green Larsen and Ryan Williams · 2017
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Low rank approximation with entrywise ℓ 1 \ell_{1} -norm error
Zhao Song, David P Woodruff, and Peilin Zhong · 2017
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Subspace embedding and linear regression with orlicz norm
Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, and Ruiqi Zhong · 2018
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Tight cell probe bounds for succinct boolean matrix-vector multiplication
Diptarka Chakraborty, Lior Kamma, and Kasper Green Larsen · 2018
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Improved rectangular matrix multiplication using powers of the coppersmith-winograd tensor
François Le Gall and Florent Urrutia · 2018
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Syntf: Synthetic and differentially private term frequency vectors for privacy-preserving text mining
Benjamin Weggenmann and Florian Kerschbaum · 2018
Cited alongside, same era.
Bourgan: generative networks with metric embeddings
Chang Xiao, Peilin Zhong, and Changxi Zheng · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Solving linear programs in the current matrix multiplication time
Michael B Cohen, Yin Tat Lee, and Zhao Song · 2019
Cited alongside, same era.
Total least squares regression in input sparsity time
Huaian Diao, Zhao Song, David Woodruff, and Xin Yang · 2019
Cited alongside, same era.
Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
Dynamic algorithms against an adaptive adversary: Generic constructions and lower bounds
Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, and Uri Stemmer · 2022
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Calibration with privacy in peer review
Wenxin Ding, Gautam Kamath, Weina Wang, and Nihar B. Shah · 2022
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Discrepancy minimization in input-sparsity time
Yichuan Deng, Zhao Song, and Omri Weinstein · 2022
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Fast distance oracles for any symmetric norm
Yichuan Deng, Zhao Song, Omri Weinstein, and Ruizhe Zhang · 2022
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A faster small treewidth sdp solver
Yuzhou Gu and Zhao Song · 2022
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A faster quantum algorithm for semidefinite programming via robust ipm framework
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Cited alongside, same era.
Differentially private algorithms for learning mixtures of separated gaussians
Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman · 2019
Cited alongside, same era.
Solving empirical risk minimization in the current matrix multiplication time
Yin Tat Lee, Zhao Song, and Qiuyi Zhang · 2019
Cited alongside, same era.
Matrix theory: optimization, concentration, and algorithms
Zhao Song · 2019
Cited alongside, same era.
Relative error tensor low rank approximation
Zhao Song, David P Woodruff, and Peilin Zhong · 2019
Cited alongside, same era.
Dp-cgan: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
Cited alongside, same era.
Solving tall dense linear programs in nearly linear time
Jan van den Brand, Yin Tat Lee, Aaron Sidford, and Zhao Song · 2020
Cited alongside, same era.
Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao, and Ruizhe Zhang · 2022
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Solving sdp faster: A robust ipm framework and efficient implementation
Baihe Huang, Shunhua Jiang, Zhao Song, Runzhou Tao, and Ruizhe Zhang · 2022
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Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism
Samuel B. Hopkins, Gautam Kamath, and Mahbod Majid · 2022
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Training overparametrized neural networks in sublinear time
Hang Hu, Zhao Song, Omri Weinstein, and Danyang Zhuo · 2022
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A faster interior-point method for sum-of-squares optimization
Shunhua Jiang, Bento Natura, and Omri Weinstein · 2022
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Improved rates for differentially private stochastic convex optimization with heavy-tailed data
Gautam Kamath, Xingtu Liu, and Huanyu Zhang · 2022
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The role of adaptive optimizers for honest private hyperparameter selection
Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, and Om Thakkar · 2022
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Dynamic tensor product regression
Aravind Reddy, Zhao Song, and Lichen Zhang · 2022
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Sparse fourier transform over lattices: A unified approach to signal reconstruction
Zhao Song, Baocheng Sun, Omri Weinstein, and Ruizhe Zhang · 2022
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Dpauc: Differentially private auc computation in federated learning
Jiankai Sun, Xin Yang, Yuanshun Yao, Junyuan Xie, Di Wu, and Chong Wang · 2022
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Faster algorithm for structured john ellipsoid computation
Zhao Song, Xin Yang, Yuanyuan Yang, and Tianyi Zhou · 2022
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Differentially private multi-party data release for linear regression
Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q Weinberger, and Chong Wang · 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang · 2022
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Differentially private label protection in split learning
Xin Yang, Jiankai Sun, Yuanshun Yao, Junyuan Xie, and Chong Wang · 2022
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Algorithm and hardness for dynamic attention maintenance in large language models
Jan van den Brand, Zhao Song, and Tianyi Zhou · 2023
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Attention scheme inspired softmax regression
Yichuan Deng, Zhihang Li, and Zhao Song · 2023
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An improved sample complexity for rank-1 matrix sensing
Yichuan Deng, Zhihang Li, and Zhao Song · 2023
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Yichuan Deng, Sridhar Mahadevan, and Zhao Song · 2023
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An over-parameterized exponential regression
Yeqi Gao, Sridhar Mahadevan, and Zhao Song · 2023
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Differentially private attention computation
Yeqi Gao, Zhao Song, and Xin Yang · 2023
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An iterative algorithm for rescaled hyperbolic functions regression
Yeqi Gao, Zhao Song, and Junze Yin · 2023
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Low rank matrix completion via robust alternating minimization in nearly linear time
Yuzhou Gu, Zhao Song, Junze Yin, and Lichen Zhang · 2023
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Robustness implies privacy in statistical estimation
Samuel B. Hopkins, Gautam Kamath, Mahbod Majid, and Shyam Narayanan · 2023
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Convex minimization with integer minima in O ~ ( n 4 ) \widetilde{O}(n^{4}) time
Haotian Jiang, Yin Tat Lee, Zhao Song, and Lichen Zhang · 2023
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The complexity of dynamic least-squares regression
Shunhua Jiang, Binghui Peng, and Omri Weinstein · 2023
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The closeness of in-context learning and weight shifting for softmax regression
Shuai Li, Zhao Song, Yu Xia, Tong Yu, and Tianyi Zhou · 2023
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Solving regularized exp, cosh and sinh regression problems
Zhihang Li, Zhao Song, and Tianyi Zhou · 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
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