R. M. G., Mark Schmidt, Francis Bach, Peter Richtarik
Variance-Reduced Methods for Machine Learning, Proceedings of the IEEE, vol. 108, no. 11, pp. 1968-1983, Nov. 2020.
Preprint
Journal
Rui Yuan, Alessandro Lazaric, R. M. G.
Sketched Newton-Raphson, ICML 2020 workshop ``Beyond first order methods in ML systems'', 2020.
Preprint
Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, R. M. G., Peter Richtárik.
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization, 2020.
Preprint
R. M. G., Othmane Sebbouh, Nicolas Loizou
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation, 2020.
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Aaron Defazio, R. M. G.
Factorial Powers for Stochastic Optimization, 2020.
Preprint
Othmane Sebbouh, R. M. G., Aaron Defazio.
On the convergence of the Stochastic Heavy Ball
Method, 2020.
Preprint
Dmitry Kovalev, R. M. G., Peter Richtárik, Alexander Rogozin.
Fast Linear Convergence of Randomized BFGS, 2020.
Preprint
R. M. G., Denali Molitor, Jacob Moorman, Deanna Needell.
Adaptive Sketch-and-Project Methods for Solving Linear Systems, 2019.
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O. Sebbouh, N. Gazagnadou, S. Jelassi, F. Bach, R. M. G.
Towards closing the gap between the theory and practice of SVRG, Neurips 2019.
Preprint
Code
Poster
Proceedings
R. M. G., N. Loizou, X. Qian, A. Sailanbayev, E. Shulgin, P. Richtárik.
SGD: general analysis and improved rates, (extended oral presentation) ICML 2019.
Preprint
Proceedings
A. Bibi, A. Sailanbayev, B. Ghanem, R. M. G. and P. Richtárik.
Improving SAGA via a probabilistic interpolation with gradient descent, 2018.
Preprint
B. K. Abid and R. M. G..
Greedy stochastic algorithms for entropy-regularized optimal transport problems, AISTATS, 2018.
Preprint
Proceedings
Poster
R. M. G. and P. Richtárik.
Randomized quasi-Newton updates are linearly convergent matrix inversion algorithms, SIAM Journal on Matrix Analysis and Applications, 2017.
Preprint
Code
Journal
Slides
R. M. G.
Sketch and Project: Randomized Iterative Methods for Linear Systems and Inverting Matrices, PhD Dissertation, School of Mathematics, The University of Edinburgh, 2016.
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Code
Slides
R. M. G. and M. P. Mello.
Computing the sparsity pattern of Hessians using automatic differentiation, ACM Transactions on Mathematical Software, 2014.
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Journal
Code
R. M. G. and M. P. Mello.
A new framework for Hessian automatic differentiation, Optimization Methods and Software, 2012.
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Journal
Slides
Code