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ORCIDRobert M. Gower
Person information
- affiliation: Télécom Paris, Institut Polytechnique de Paris, France
- affiliation (PhD 2016): University of Edinburgh, School of Mathematics, Edinburgh, UK
- affiliation: State University of Campinas, Department of Applied Mathematics, Campinas, Brazil
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2020 – today
- 2024
[i37]Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert M. Gower:
SGD with Clipping is Secretly Estimating the Median Gradient. CoRR abs/2402.12828 (2024)- [i36]Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. CoRR abs/2402.14758 (2024) - [i35]Aaron Mishkin, Alberto Bietti, Robert M. Gower:
Level Set Teleportation: An Optimization Perspective. CoRR abs/2403.03362 (2024) - [i34]Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower:
Directional Smoothness and Gradient Methods: Convergence and Adaptivity. CoRR abs/2403.04081 (2024) - 2023
- [j14]Ahmed Khaled
, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik:
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. J. Optim. Theory Appl. 199(2): 499-540 (2023) - [j13]Fabian Schaipp, Robert M. Gower, Michael Ulbrich:
A Stochastic Proximal Polyak Step Size. Trans. Mach. Learn. Res. 2023 (2023) - [c18]Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower:
SP2 : A Second Order Stochastic Polyak Method. ICLR 2023 - [c17]Rui Yuan, Simon Shaolei Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao:
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. ICLR 2023 - [c16]Si Yi Meng, Robert M. Gower:
A Model-Based Method for Minimizing CVaR and Beyond. ICML 2023: 24436-24456 - [c15]Justin Domke, Robert M. Gower, Guillaume Garrigos:
Provable convergence guarantees for black-box variational inference. NeurIPS 2023 - [c14]Chirag Modi, Robert M. Gower, Charles Margossian, Yuling Yao, David M. Blei, Lawrence K. Saul:
Variational Inference with Gaussian Score Matching. NeurIPS 2023 - [i33]Fabian Schaipp, Robert M. Gower, Michael Ulbrich:
A Stochastic Proximal Polyak Step Size. CoRR abs/2301.04935 (2023) - [i32]Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower:
MoMo: Momentum Models for Adaptive Learning Rates. CoRR abs/2305.07583 (2023) - [i31]Bo Zhao, Robert M. Gower, Robin Walters, Rose Yu:
Improving Convergence and Generalization Using Parameter Symmetries. CoRR abs/2305.13404 (2023) - [i30]Si Yi Meng, Robert M. Gower:
A Model-Based Method for Minimizing CVaR and Beyond. CoRR abs/2305.17498 (2023) - [i29]Justin Domke, Guillaume Garrigos, Robert M. Gower:
Provable convergence guarantees for black-box variational inference. CoRR abs/2306.03638 (2023) - [i28]Chirag Modi, Charles Margossian, Yuling Yao, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Variational Inference with Gaussian Score Matching. CoRR abs/2307.07849 (2023) - [i27]Guillaume Garrigos, Robert M. Gower, Fabian Schaipp:
Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM. CoRR abs/2307.14528 (2023) - [i26]Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov, Robert M. Gower, Martin Takác:
SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms. CoRR abs/2312.17369 (2023) - 2022
- [j12]Rui Yuan, Alessandro Lazaric, Robert M. Gower:
Sketched Newton-Raphson. SIAM J. Optim. 32(3): 1555-1583 (2022) - [j11]Nidham Gazagnadou, Mark Ibrahim, Robert M. Gower:
RidgeSketch: A Fast Sketching Based Solver for Large Scale Ridge Regression. SIAM J. Matrix Anal. Appl. 43(3): 1440-1468 (2022) - [j10]Zheng Wang, Robert M. Gower, Yili Xia, Lanxin He, Yongming Huang:
Randomized Iterative Methods for Low-Complexity Large-Scale MIMO Detection. IEEE Trans. Signal Process. 70: 2934-2949 (2022) - [j9]Zheng Wang, Robert M. Gower, Cheng Zhang, Shanxiang Lyu, Yili Xia, Yongming Huang:
A Statistical Linear Precoding Scheme Based on Random Iterative Method for Massive MIMO Systems. IEEE Trans. Wirel. Commun. 21(12): 10115-10129 (2022) - [c13]Jiabin Chen, Rui Yuan, Guillaume Garrigos, Robert M. Gower:
SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums. AISTATS 2022: 279-318 - [c12]Rui Yuan, Robert M. Gower, Alessandro Lazaric:
A general sample complexity analysis of vanilla policy gradient. AISTATS 2022: 3332-3380 - [i25]Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou, Fabian Pedregosa:
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes. CoRR abs/2202.12328 (2022) - [i24]Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower:
SP2: A Second Order Stochastic Polyak Method. CoRR abs/2207.08171 (2022) - [i23]Rui Yuan, Simon S. Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao:
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. CoRR abs/2210.01400 (2022) - 2021
- [j8]Robert M. Gower, Peter Richtárik, Francis R. Bach:
Stochastic quasi-gradient methods: variance reduction via Jacobian sketching. Math. Program. 188(1): 135-192 (2021) - [j7]Robert M. Gower, Denali Molitor, Jacob D. Moorman, Deanna Needell:
On Adaptive Sketch-and-Project for Solving Linear Systems. SIAM J. Matrix Anal. Appl. 42(2): 954-989 (2021) - [c11]Aaron Defazio, Robert M. Gower:
The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization. ACML 2021: 49-64 - [c10]Robert M. Gower, Othmane Sebbouh, Nicolas Loizou:
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. AISTATS 2021: 1315-1323 - [c9]Othmane Sebbouh, Robert M. Gower, Aaron Defazio:
Almost sure convergence rates for Stochastic Gradient Descent and Stochastic Heavy Ball. COLT 2021: 3935-3971 - [i22]Jiabin Chen, Rui Yuan, Guillaume Garrigos, Robert M. Gower:
SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums. CoRR abs/2106.10520 (2021) - [i21]Robert M. Gower, Aaron Defazio, Michael G. Rabbat:
Stochastic Polyak Stepsize with a Moving Target. CoRR abs/2106.11851 (2021) - [i20]Rui Yuan, Robert M. Gower, Alessandro Lazaric:
A general sample complexity analysis of vanilla policy gradient. CoRR abs/2107.11433 (2021) - 2020
- [j6]Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik:
Variance-Reduced Methods for Machine Learning. Proc. IEEE 108(11): 1968-1983 (2020) - [i19]Dmitry Kovalev, Robert M. Gower, Peter Richtárik, Alexander Rogozin:
Fast Linear Convergence of Randomized BFGS. CoRR abs/2002.11337 (2020) - [i18]Aaron Defazio, Robert M. Gower:
Factorial Powers for Stochastic Optimization. CoRR abs/2006.01244 (2020) - [i17]Othmane Sebbouh, Robert M. Gower, Aaron Defazio:
On the convergence of the Stochastic Heavy Ball Method. CoRR abs/2006.07867 (2020) - [i16]Robert M. Gower, Othmane Sebbouh, Nicolas Loizou:
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. CoRR abs/2006.10311 (2020) - [i15]Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik:
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. CoRR abs/2006.11573 (2020) - [i14]Rui Yuan, Alessandro Lazaric, Robert M. Gower:
Sketched Newton-Raphson. CoRR abs/2006.12120 (2020) - [i13]Robert M. Gower, Margarida P. Mello:
A new framework for the computation of Hessians. CoRR abs/2007.15040 (2020) - [i12]Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik:
Variance-Reduced Methods for Machine Learning. CoRR abs/2010.00892 (2020)
2010 – 2019
- 2019
- [c8]Nidham Gazagnadou, Robert M. Gower, Joseph Salmon:
Optimal Mini-Batch and Step Sizes for SAGA. ICML 2019: 2142-2150 - [c7]Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin:
SGD with Arbitrary Sampling: General Analysis and Improved Rates. ICML 2019: 5200-5209 - [c6]Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik:
RSN: Randomized Subspace Newton. NeurIPS 2019: 614-623 - [c5]Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower:
Towards closing the gap between the theory and practice of SVRG. NeurIPS 2019: 646-656 - [i11]Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik:
SGD: General Analysis and Improved Rates. CoRR abs/1901.09401 (2019) - [i10]Nidham Gazagnadou, Robert M. Gower, Joseph Salmon:
Optimal mini-batch and step sizes for SAGA. CoRR abs/1902.00071 (2019) - [i9]Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower:
Towards closing the gap between the theory and practice of SVRG. CoRR abs/1908.02725 (2019) - [i8]Robert M. Gower, Denali Molitor, Jacob D. Moorman, Deanna Needell:
Adaptive Sketch-and-Project Methods for Solving Linear Systems. CoRR abs/1909.03604 (2019) - 2018
- [c4]Robert M. Gower, Nicolas Le Roux, Francis R. Bach:
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. AISTATS 2018: 707-715 - [c3]Brahim Khalil Abid, Robert M. Gower:
Stochastic algorithms for entropy-regularized optimal transport problems. AISTATS 2018: 1505-1512 - [c2]Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich:
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. NeurIPS 2018: 1626-1636 - [i7]Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich:
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. CoRR abs/1802.04079 (2018) - [i6]Brahim Khalil Abid, Robert M. Gower:
Greedy stochastic algorithms for entropy-regularized optimal transport problems. CoRR abs/1803.01347 (2018) - 2017
- [j5]Robert M. Gower, Peter Richtárik:
Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms. SIAM J. Matrix Anal. Appl. 38(4): 1380-1409 (2017) - [i5]Robert M. Gower, Nicolas Le Roux, Francis R. Bach:
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. CoRR abs/1710.07462 (2017) - 2016
- [j4]Robert Mansel Gower, Artur L. Gower:
Higher-order reverse automatic differentiation with emphasis on the third-order. Math. Program. 155(1-2): 81-103 (2016) - [c1]Robert M. Gower, Donald Goldfarb, Peter Richtárik:
Stochastic Block BFGS: Squeezing More Curvature out of Data. ICML 2016: 1869-1878 - [i4]Robert M. Gower, Peter Richtárik:
Randomized Quasi-Newton Updates are Linearly Convergent Matrix Inversion Algorithms. CoRR abs/1602.01768 (2016) - 2015
- [j3]Robert Mansel Gower, Peter Richtárik:
Randomized Iterative Methods for Linear Systems. SIAM J. Matrix Anal. Appl. 36(4): 1660-1690 (2015) - [i3]Robert Mansel Gower, Peter Richtárik:
Stochastic Dual Ascent for Solving Linear Systems. CoRR abs/1512.06890 (2015) - 2014
- [j2]Robert Mansel Gower, Margarida Pinheiro Mello:
Computing the sparsity pattern of Hessians using automatic differentiation. ACM Trans. Math. Softw. 40(2): 10:1-10:15 (2014) - [i2]Robert Mansel Gower, Jacek Gondzio:
Action constrained quasi-Newton methods. CoRR abs/1412.8045 (2014) - 2013
- [i1]Robert Mansel Gower, Artur L. Gower:
Higher-order Reverse Automatic Differentiation with emphasis on the third-order. CoRR abs/1309.5479 (2013) - 2012
- [j1]Robert M. Gower, Margarida P. Mello:
A new framework for the computation of Hessians. Optim. Methods Softw. 27(2): 251-273 (2012)
Coauthor Index
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