J. T. Abatzoglou, S. Z. Dobrowski, S. A. Parks, and K. C. Hegewisch, Data descriptor: Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data, vol.5, 2018.

T. G. Addair, D. A. Dodge, W. R. Walter, and S. D. Ruppert, Large-scale seismic signal analysis with hadoop, Computers and Geosciences, vol.66, p.2014

N. Ailon and B. Chazelle, The fast johnson-lindenstrauss transform and approximate nearest neighbors, SIAM J. Comput, vol.39, issue.1, pp.302-322, 2009.

D. P. Bertsekas, Constrained Optimization and Lagrange Multiplier Methods (Optimization and Neural Computation Series), Athena Scientific, 1996.

H. Richard, G. M. Byrd, W. Chin, J. Neveitt, and . Nocedal, On the use of stochastic Hessian information in optimization methods for machine learning, SIAM Journal on Optimization, vol.21, issue.3, pp.977-995, 2011.

C. Chih, C. Chang, and . Lin, LIBSVM : A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-27, 2011.

B. Christianson, Automatic Hessians by reverse accumulation, IMA Journal of Numerical Analysis, vol.12, issue.2, pp.135-150, 1992.

J. R. Cole, Q. Wang, J. A. Fish, B. Chai, D. M. Mcgarrell et al., Ribosomal Database Project: data and tools for high throughput rRNA analysis, Nucleic Acids Research, vol.42, issue.D1, p.2013

A. R. Conn, N. I. Gould, and P. L. Toint, Trust-region Methods. Society for Industrial and Applied Mathematics, 2000.

G. Alexandre-d'aspremont, M. Cristóbal, and . Jaggi, Optimal affine-invariant smooth minimization algorithms, SIAM Journal on Optimization, vol.28, issue.3, pp.2384-2405, 2018.

R. S. Dembo, S. C. Eisenstat, and T. Steihaug, Inexact Newton methods, SIAM Journal on Numerical Analysis, vol.19, issue.2, pp.400-408, 1982.

N. Doikov and P. Richtárik, Randomized block cubic Newton method, Proceedings of the 35th International Conference on Machine Learning, 2018.

K. Fountoulakis and R. Tappenden, A flexible coordinate descent method, Computational Optimization and Applications, vol.70, issue.2, pp.351-394, 2018.

R. Gower and M. Mello, A new framework for the computation of hessians, Optimization Methods and Software, vol.27, issue.2, pp.251-273, 2012.

R. M. Gower, D. Goldfarb, and P. Richtárik, Stochastic block BFGS: Squeezing more curvature out of data, Proceedings of the 33rd International Conference on Machine Learning, 2016.

M. Robert, P. Gower, and . Richtárik, Randomized iterative methods for linear systems, SIAM Journal on Matrix Analysis and Applications, vol.36, issue.4, pp.1660-1690, 2015.

W. Johnson and J. Lindenstrauss, Extensions of Lipschitz mappings into a Hilbert space, Conference in modern analysis and probability, vol.26, pp.189-206, 1982.

S. U. Sai-praneeth-karimireddy, M. Stich, and . Jaggi, Global linear convergence of Newtons method without strong-convexity or Lipschitz gradients, 2018.

C. H. Lee and H. J. Yoon, Medical big data: promise and challenges. kidney research and clinical practice, Kidney Res Clin Pract, vol.36, issue.4, pp.3-4, 2017.

H. Lu, R. M. Freund, and Y. Nesterov, Relatively smooth convex optimization by first-order methods, and applications, SIAM Journal on Optimization, vol.28, issue.1, pp.333-354, 2018.

H. Luo, A. Agarwal, N. Cesa-bianchi, and J. Langford, Efficient second order online learning by sketching, Advances in Neural Information Processing Systems, vol.29, pp.902-910, 2016.

Y. Nesterov and A. Nemirovskii, Interior Point Polynomial Algorithms in Convex Programming, Studies in Applied Mathematics. Society for Industrial and Applied Mathematics, 1987.

Y. Nesterov, Efficiency of coordinate descent methods on huge-scale optimization problems, SIAM Journal on Optimization, vol.22, issue.2, pp.341-362, 2012.

Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, 2014.

Y. Nesterov and B. T. Polyak, Cubic regularization of Newton method and its global performance, Mathematical Programming, vol.108, issue.1, pp.177-205, 2006.

R. A. Overbeek, N. Larsen, G. D. Pusch, D. Mark, E. Souza et al., WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction, Nucleic Acids Research, vol.28, issue.1, pp.123-125, 2000.

M. Pilanci and M. J. Wainwright, Iterative Hessian sketch : Fast and accurate solution approximation for constrained least-squares, Journal of Machine Learning Research, vol.17, pp.1-33, 2016.

M. Pilanci and M. J. Wainwright, Newton sketch: A near linear-time optimization algorithm with linear-quadratic convergence, SIAM Journal on Optimization, vol.27, issue.1, pp.205-245, 2017.

Z. Qu, P. Richtárik, M. Taká?, and O. Fercoq, SDNA: Stochastic dual Newton ascent for empirical risk minimization, Proceedings of the 33rd International Conference on Machine Learning, 2016.

P. Richtárik and M. Taká?, Stochastic reformulations of linear systems: algorithms and convergence theory, 2017.

J. Vanschoren, J. N. Van-rijn, B. Bischl, and L. Torgo, Openml: Networked science in machine learning, SIGKDD Explorations, vol.15, issue.2, pp.49-60, 2013.

A. Max and . Woodbury, Inverting modified matrices, 1950.

J. Tjalling and . Ypma, Historical development of the newton-raphson method, SIAM Rev, vol.37, issue.4, pp.531-551, 1995.