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Robert Mansel Gower
Robert Mansel Gower
Research Scientist, Center for Computational Mathematics, Flatiron Institute, Simons Foundation
Verified email at flatironinstitute.org - Homepage
Title
Cited by
Cited by
Year
SGD: General analysis and improved rates
RM Gower, N Loizou, X Qian, A Sailanbayev, E Shulgin, P Richtárik
Proceedings of the 36 the International Conference on Machine Learning, 2019
3982019
Randomized iterative methods for linear systems
RM Gower, P Richtárik
SIAM Journal on Matrix Analysis and Applications 36 (4), 1660-1690, 2015
3042015
Stochastic block BFGS: Squeezing more curvature out of data
RM Gower, D Goldfarb, P Richtárik
International Conference on Machine Learning, 1869-1878, 2016
1882016
Stochastic quasi-gradient methods: Variance reduction via Jacobian sketching
RM Gower, P Richtárik, F Bach
Mathematical Programming, 2020
1052020
Variance-reduced methods for machine learning
RM Gower, M Schmidt, F Bach, P Richtárik
Proceedings of the IEEE 108 (11), 1968-1983, 2020
1022020
Randomized quasi-Newton updates are linearly convergent matrix inversion algorithms
RM Gower, P Richtárik
SIAM Journal on Matrix Analysis and Applications 38 (4), 1380-1409, 2017
972017
Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball
O Sebbouh, RM Gower, A Defazio
Conference on Learning Theory, 3935-3971, 2021
92*2021
RSN: randomized subspace Newton
RM Gower, D Kovalev, F Lieder, P Richtárik
Advances in Neural Information Processing Systems 32, 2019
832019
Stochastic dual ascent for solving linear systems
RM Gower, P Richtárik
arXiv preprint arXiv:1512.06890, 2015
722015
Sgd for structured nonconvex functions: Learning rates, minibatching and interpolation
RM Gower, O Sebbouh, N Loizou
International Conference on Artificial Intelligence and Statistics, 1315-1323, 2021
712021
Handbook of convergence theorems for (stochastic) gradient methods
G Garrigos, RM Gower
arXiv preprint arXiv:2301.11235, 2023
66*2023
On adaptive sketch-and-project for solving linear systems
RM Gower, D Molitor, J Moorman, D Needell
SIAM Journal on Matrix Analysis and Applications 42 (2), 954-989, 2021
49*2021
Accelerated stochastic matrix inversion: general theory and speeding up BFGS rules for faster second-order optimization
RM Gower, F Hanzely, P Richtárik, SU Stich
Advances in Neural Information Processing Systems 31, 2018
472018
Optimal mini-batch and step sizes for saga
N Gazagnadou, RM Gower, J Salmon
International conference on machine learning, 2142-2150, 2019
402019
A general sample complexity analysis of vanilla policy gradient
R Yuan, RM Gower, A Lazaric
International Conference on Artificial Intelligence and Statistics, 3332-3380, 2022
382022
Unified analysis of stochastic gradient methods for composite convex and smooth optimization
A Khaled, O Sebbouh, N Loizou, RM Gower, P Richtárik
Journal of Optimization Theory and Applications 199 (2), 499-540, 2023
372023
Stochastic algorithms for entropy-regularized optimal transport problems
BK Abid, RM Gower
International Conference on Artificial Intelligence and Statistics, 1505-1512, 2018
302018
Tracking the gradients using the hessian: A new look at variance reducing stochastic methods
RM Gower, N Le Roux, F Bach
International Conference on Artificial Intelligence and Statistics, 707-715, 2018
282018
Sketched Newton--Raphson
R Yuan, A Lazaric, RM Gower
SIAM Journal on Optimization 32 (3), 1555-1583, 2022
26*2022
A new framework for the computation of Hessians
RM Gower, MP Mello
Optimization Methods and Software 27 (2), 251-273, 2012
262012
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