GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
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Shusen Wang | Peng Xu | Michael W. Mahoney | Farbod Roosta-Khorasani | Farbod Roosta-Khorasani | Shusen Wang | Peng Xu
[1] D K Smith,et al. Numerical Optimization , 2001, J. Oper. Res. Soc..
[2] S. Sathiya Keerthi,et al. An efficient distributed learning algorithm based on effective local functional approximations , 2018, J. Mach. Learn. Res..
[3] Stephen J. Wright,et al. An asynchronous parallel stochastic coordinate descent algorithm , 2013, J. Mach. Learn. Res..
[4] Dimitris Achlioptas,et al. Database-friendly random projections: Johnson-Lindenstrauss with binary coins , 2003, J. Comput. Syst. Sci..
[5] Petros Drineas,et al. Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication , 2006, SIAM J. Comput..
[6] Leslie G. Valiant,et al. A bridging model for parallel computation , 1990, CACM.
[7] Michael W. Mahoney. Randomized Algorithms for Matrices and Data , 2011, Found. Trends Mach. Learn..
[8] Alexander J. Smola,et al. Parallelized Stochastic Gradient Descent , 2010, NIPS.
[9] W. B. Johnson,et al. Extensions of Lipschitz mappings into Hilbert space , 1984 .
[10] S. Muthukrishnan,et al. Faster least squares approximation , 2007, Numerische Mathematik.
[11] Ohad Shamir,et al. Distributed stochastic optimization and learning , 2014, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[12] Jacek Gondzio,et al. Exploiting separability in large-scale linear support vector machine training , 2011, Comput. Optim. Appl..
[13] Carlos Guestrin,et al. Distributed GraphLab : A Framework for Machine Learning and Data Mining in the Cloud , 2012 .
[14] Peng Xu,et al. Newton-type methods for non-convex optimization under inexact Hessian information , 2017, Math. Program..
[15] Martin J. Wainwright,et al. Communication-efficient algorithms for statistical optimization , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).
[16] Stephen J. Wright,et al. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent , 2011, NIPS.
[17] Martin J. Wainwright,et al. Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares , 2014, J. Mach. Learn. Res..
[18] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..
[19] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[20] Jorge Nocedal,et al. On the limited memory BFGS method for large scale optimization , 1989, Math. Program..
[21] Martin J. Wainwright,et al. Newton Sketch: A Near Linear-Time Optimization Algorithm with Linear-Quadratic Convergence , 2015, SIAM J. Optim..
[22] Peter Richtárik,et al. Distributed Coordinate Descent Method for Learning with Big Data , 2013, J. Mach. Learn. Res..
[23] Shusen Wang,et al. Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging , 2017, ICML.
[24] David P. Woodruff,et al. Fast approximation of matrix coherence and statistical leverage , 2011, ICML.
[25] John Darzentas,et al. Problem Complexity and Method Efficiency in Optimization , 1983 .
[26] S. Sundararajan,et al. A Parallel SGD method with Strong Convergence , 2013, ArXiv.
[27] Uri M. Ascher,et al. Data completion and stochastic algorithms for PDE inversion problems with many measurements , 2013, ArXiv.
[28] Scott Shenker,et al. Spark: Cluster Computing with Working Sets , 2010, HotCloud.
[29] Boris T. Polyak,et al. Newton's method and its use in optimization , 2007, Eur. J. Oper. Res..
[30] Tong Zhang,et al. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction , 2013, NIPS.
[31] Olivier Chapelle,et al. Training a Support Vector Machine in the Primal , 2007, Neural Computation.
[32] Ion Necoara,et al. Parallel Random Coordinate Descent Method for Composite Minimization: Convergence Analysis and Error Bounds , 2016, SIAM J. Optim..
[33] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[34] Peter Richtárik,et al. Parallel coordinate descent methods for big data optimization , 2012, Mathematical Programming.
[35] Yuchen Zhang,et al. DiSCO: Distributed Optimization for Self-Concordant Empirical Loss , 2015, ICML.
[36] Sanjay Ghemawat,et al. MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.
[37] Y. Nesterov. A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .
[38] Zhihua Zhang,et al. SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions , 2014, J. Mach. Learn. Res..
[39] Joel A. Tropp,et al. Improved Analysis of the subsampled Randomized Hadamard Transform , 2010, Adv. Data Sci. Adapt. Anal..
[40] Ameet Talwalkar,et al. Federated Multi-Task Learning , 2017, NIPS.
[41] Peter Richtárik,et al. Federated Optimization: Distributed Machine Learning for On-Device Intelligence , 2016, ArXiv.
[42] David P. Woodruff,et al. Low rank approximation and regression in input sparsity time , 2013, STOC '13.
[43] Dean P. Foster,et al. Faster Ridge Regression via the Subsampled Randomized Hadamard Transform , 2013, NIPS.
[44] James Demmel,et al. Avoiding communication in primal and dual block coordinate descent methods , 2016, SIAM J. Sci. Comput..
[45] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[46] Tianbao Yang,et al. Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent , 2013, NIPS.
[47] Uri M. Ascher,et al. Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables , 2014, SIAM/ASA J. Uncertain. Quantification.
[48] Sarvar Patel,et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..
[49] Michael W. Mahoney,et al. Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments , 2015, Proceedings of the IEEE.
[50] Ohad Shamir,et al. Communication-Efficient Distributed Optimization using an Approximate Newton-type Method , 2013, ICML.
[51] Joel A. Tropp,et al. An Introduction to Matrix Concentration Inequalities , 2015, Found. Trends Mach. Learn..
[52] Ameet Talwalkar,et al. MLlib: Machine Learning in Apache Spark , 2015, J. Mach. Learn. Res..
[53] Alexander J. Smola,et al. AIDE: Fast and Communication Efficient Distributed Optimization , 2016, ArXiv.
[54] S. Sathiya Keerthi,et al. A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs , 2005, J. Mach. Learn. Res..
[55] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[56] Ping Ma,et al. A statistical perspective on algorithmic leveraging , 2013, J. Mach. Learn. Res..
[57] Tianbao Yang,et al. Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity , 2015 .
[58] S. Muthukrishnan,et al. Sampling algorithms for l2 regression and applications , 2006, SODA '06.
[59] Alexander J. Smola,et al. Scaling Distributed Machine Learning with the Parameter Server , 2014, OSDI.
[60] Peter Richtárik,et al. Optimization in High Dimensions via Accelerated, Parallel, and Proximal Coordinate Descent , 2016, SIAM Rev..
[61] Thomas Hofmann,et al. Communication-Efficient Distributed Dual Coordinate Ascent , 2014, NIPS.
[62] Michael I. Jordan,et al. Adding vs. Averaging in Distributed Primal-Dual Optimization , 2015, ICML.
[63] Peng Xu,et al. Sub-sampled Newton Methods with Non-uniform Sampling , 2016, NIPS.
[64] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[65] Tong Zhang,et al. A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization , 2016, J. Mach. Learn. Res..
[66] Michael W. Mahoney,et al. Revisiting the Nystrom Method for Improved Large-scale Machine Learning , 2013, J. Mach. Learn. Res..
[67] Michael W. Mahoney,et al. Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression , 2012, STOC '13.
[68] Olvi L. Mangasarian,et al. A finite newton method for classification , 2002, Optim. Methods Softw..
[69] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[70] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[71] Michael W. Mahoney,et al. Sub-Sampled Newton Methods I: Globally Convergent Algorithms , 2016, ArXiv.
[72] Farbod Roosta-Khorasani. Randomized Algorithms for Solving Large Scale Nonlinear Least Squares Problems , 2015 .
[73] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[74] Uri M. Ascher,et al. Stochastic Algorithms for Inverse Problems Involving PDEs and many Measurements , 2014, SIAM J. Sci. Comput..
[75] Yves Tillé,et al. Sampling Algorithms , 2011, International Encyclopedia of Statistical Science.
[76] Peng Xu,et al. Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study , 2017, SDM.
[77] Michael I. Jordan,et al. CoCoA: A General Framework for Communication-Efficient Distributed Optimization , 2016, J. Mach. Learn. Res..
[78] Alexander J. Smola,et al. On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants , 2015, NIPS.
[79] David P. Woodruff. Sketching as a Tool for Numerical Linear Algebra , 2014, Found. Trends Theor. Comput. Sci..
[80] Michael W. Mahoney,et al. Sub-Sampled Newton Methods II: Local Convergence Rates , 2016, ArXiv.
[81] Huy L. Nguyen,et al. OSNAP: Faster Numerical Linear Algebra Algorithms via Sparser Subspace Embeddings , 2012, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.