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In distributed second order optimization, a standard strategy is to average many local estimates, each of which is based on a small sketch or batch of the data.
Sampling algorithms for ℓ 2 \ell_{2} regression and applications
Petros Drineas, Michael W Mahoney, and S Muthukrishnan · 2006
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Negative dependence and the geometry of polynomials
Julius Borcea, Petter Brändén, and Thomas Liggett · 2009
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Matrix Mathematics: Theory, Facts, and Formulas
Dennis S. Bernstein · 2011
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Fast approximation of matrix coherence and statistical leverage
Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, and David P. Woodruff · 2012
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The masked sample covariance estimator: an analysis using matrix concentration inequalities
Alex Gittens, Richard Y. Chen, and Joel A. Tropp · 2012
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Determinantal Point Processes for Machine Learning
Alex Kulesza and Ben Taskar · 2012
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User-friendly tail bounds for sums of random matrices
Joel A. Tropp · 2012
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Communication-efficient distributed optimization using an approximate Newton-type method
Ohad Shamir, Nati Srebro, and Tong Zhang · 2014
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Fast randomized kernel ridge regression with statistical guarantees
Ahmed El Alaoui and Michael W. Mahoney · 2015
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Randomized sketches of convex programs with sharp guarantees
Mert Pilanci and Martin J. Wainwright · 2015
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Disco: Distributed optimization for self-concordant empirical loss
Yuchen Zhang and Xiao Lin · 2015
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Fast Randomized Algorithms for Convex Optimization and Statistical Estimation
Mert Pilanci · 2016
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Iterative hessian sketch: Fast and accurate solution approximation for constrained least-squares
Mert Pilanci and Martin J Wainwright · 2016
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AIDE: Fast and Communication Efficient Distributed Optimization
Sashank J. Reddi, Jakub Konecný, Peter Richtárik, Barnabás Póczós, and Alex Smola · 2016
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Newton-like method with diagonal correction for distributed optimization
D. Bajović, D. Jakovetić, N. Krejić, and N.K. Jerinkić · 2017
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A short note on poisson tail bounds
Clément Canonne · 2017
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Low-rank approximation and regression in input sparsity time
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Network newton distributed optimization methods
Aryan Mokhtari, Qing Ling, and Alejandro Ribeiro · 2017
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Newton sketch: A near linear-time optimization algorithm with linear-quadratic convergence
Mert Pilanci and Martin J Wainwright · 2017
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Sketched ridge regression: Optimization perspective, statistical perspective, and model averaging
Shusen Wang, Alex Gittens, and Michael W. Mahoney · 2017
Exact expressions for double descent and implicit regularization via surrogate random design
Michał Dereziński, Feynman Liang, and Michael W. Mahoney · 2019
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Distributed estimation of the inverse hessian by determinantal averaging
Michał Dereziński and Michael W Mahoney · 2019
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Correcting the bias in least squares regression with volume-rescaled sampling
Michał Dereziński, Manfred K. Warmuth, and Daniel Hsu · 2019
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Faster least squares optimization
Jonathan Lacotte and Mert Pilanci · 2019
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High-dimensional optimization in adaptive random subspaces
Jonathan Lacotte, Mert Pilanci, and Marco Pavone · 2019
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Randomized sketches for kernels: Fast and optimal nonparametric regression
Yun Yang, Mert Pilanci, Martin J Wainwright, et al · 2017
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Reverse iterative volume sampling for linear regression
Michał Dereziński and Manfred K. Warmuth · 2018
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A matrix chernoff bound for strongly rayleigh distributions and spectral sparsifiers from a few random spanning trees
Rasmus Kyng and Zhao Song · 2018
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Giant: Globally improved approximate newton method for distributed optimization
Shusen Wang, Fred Roosta, Peng Xu, and Michael W Mahoney · 2018
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Fast determinantal point processes via distortion-free intermediate sampling
Michał Dereziński · 2019
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Exact sampling of determinantal point processes with sublinear time preprocessing
Michał Dereziński, Daniele Calandriello, and Michal Valko · 2019
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Ibrahim Kurban Ozaslan, Mert Pilanci, and Orhan Arikan · 2019
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Distributed averaging methods for randomized second order optimization
Burak Bartan and Mert Pilanci · 2020
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Distributed sketching methods for privacy preserving regression
Burak Bartan and Mert Pilanci · 2020
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Sampling from a k k -dpp without looking at all items
Daniele Calandriello, Michał Dereziński, and Michal Valko · 2020
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Limiting spectrum of randomized hadamard transform and optimal iterative sketching methods
Jonathan Lacotte, Sifan Liu, Edgar Dobriban, and Mert Pilanci · 2020
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Effective dimension adaptive sketching methods for faster regularized least-squares optimization
Jonathan Lacotte and Mert Pilanci · 2020
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Optimal randomized first-order methods for least-squares problems
Jonathan Lacotte and Mert Pilanci · 2020
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Lower bounds and a near-optimal shrinkage estimator for least squares using random projections
Srivatsan Sridhar, Mert Pilanci, and Ayfer Özgür · 2020
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