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We analyze the convergence rate of the randomized Newton-like method introduced by Qu et.
A randomized coordinate descent method with volume sampling
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Exact sampling of determinantal point processes with sublinear time preprocessing
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Techniques of Variational Analysis
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Determinantal processes and independence
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Gaussian processes for machine learning
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Statistical properties of kernel principal component analysis
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Information consistency of nonparametric gaussian process methods
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Determinantal Point Processes for Machine Learning
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Accelerating stochastic gradient descent using predictive variance reduction
Johnson, R. and Zhang, T. (2013) · 2013
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Distributed coordinate descent method for learning with big data
Richtárik, P. and Takác, M. (2013) · 2013
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Stochastic dual coordinate ascent methods for regularized loss
Shalev-Shwartz, S. and Zhang, T. (2013) · 2013
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Understanding machine learning: From theory to algorithms
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Fast randomized kernel ridge regression with statistical guarantees
Alaoui, A. E. and Mahoney, M. W. (2015) · 2015
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Randomized iterative methods for linear systems
Gower, R. M. and Richtárik, P. (2015) · 2015
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Adding vs. averaging in distributed primal-dual optimization
Ma, C., Virginia, S., Jaggi, M., Jordan, M. I., Richtárik, P., and Takáč, M. (2015) · 2015
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Newton sketch: A linear-time optimization algorithm with linear-quadratic convergence
Pilanci, M. and Wainwright, M. J. (2015) · 2015
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On optimal probabilities in stochastic coordinate descent methods
Sub-Sampled Newton Methods I: Globally Convergent Algorithms
Roosta-Khorasani, F. and Mahoney, M. W. (2016) · 2016
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Sub-sampled newton methods ii: Local convergence rates
Roosta-Khorasani, F. and Mahoney, M. W. (2016) · 2016
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Efficient Sequential Learning in Structured and Constrained Environments
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Sarah: A novel method for machine learning problems using stochastic recursive gradient
Nguyen, L. M., Liu, J., Scheinberg, K., and Takáč, M. (2017) · 2017
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Determinantal point processes for mini-batch diversification
Zhang, C., Kjellström, H., and Mandt, S. (2017) · 2017
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Richtárik, P. and Takáč, M. (2015) · 2015
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Even faster accelerated coordinate descent using non-uniform sampling
Allen-Zhu, Z., Qu, Z., Richtárik, P., and Yuan, Y. (2016) · 2016
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Monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes
Anari, N., Gharan, S. O., and Rezaei, A. (2016) · 2016
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Primal-dual rates and certificates
Dünner, C., Forte, S., Takáč, M., and Jaggi, M. (2016) · 2016
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SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Qu, Z., Richtárik, P., Takáč, M., and Fercoq, O. (2016) · 2016
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Coordinate descent with arbitrary sampling ii: Expected separable overapproximation
Qu, Z. and Richtárik, P. (2016) · 2016
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Parallel coordinate descent methods for big data optimization
Richtárik, P. and Takáč, M. (2016) · 2016
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Reverse iterative volume sampling for linear regression
Dereziński, M. and Warmuth, M. K. (2018) · 2018
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Accelerated coordinate descent with arbitrary sampling and best rates for minibatches
Hanzely, F. and Richtárik, P. (2018) · 2018
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Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems
Karimireddy, S. P. R., Stich, S., and Jaggi, M. (2018) · 2018
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Parallel stochastic newton method
Mutný, M. and Richtárik, P. (2018) · 2018
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Online variance reduction with mixtures
Borsos, Z., Curi, S., Levy, K. Y., and Krause, A. (2019) · 2019
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Fast determinantal point processes via distortion-free intermediate sampling
Dereziński, M. (2019) · 2019
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RSN: Randomized subspace Newton
Gower, R., Koralev, D., Lieder, F., and Richtarik, P. (2019) · 2019
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