Robert M. Gower

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 62

Top Topics

Ridge Regression

Gradient Method

Top Venues

Publications

Aaron Mishkin, Alberto Bietti, Robert M. Gower
Level Set Teleportation: An Optimization Perspective. CoRR (2024)
Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower
Directional Smoothness and Gradient Methods: Convergence and Adaptivity. CoRR (2024)
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 (2024)
Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt, Samuel Horváth, Robert M. Gower, Martin Takác
Enhancing Policy Gradient with the Polyak Step-Size Adaption. CoRR (2024)
Robert M. Gower, Dirk A. Lorenz, Maximilian Winkler
A Bregman-Kaczmarz method for nonlinear systems of equations. Comput. Optim. Appl. 87 (3) (2024)
Bo Zhao, Robert M. Gower, Robin Walters, Rose Yu
Improving Convergence and Generalization Using Parameter Symmetries. ICLR (2024)
Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert M. Gower
SGD with Clipping is Secretly Estimating the Median Gradient. CoRR (2024)
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)
Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
MoMo: Momentum Models for Adaptive Learning Rates. CoRR (2023)
Justin Domke, Robert M. Gower, Guillaume Garrigos
Provable convergence guarantees for black-box variational inference. NeurIPS (2023)
Si Yi Meng, Robert M. Gower
A Model-Based Method for Minimizing CVaR and Beyond. ICML (2023)
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) (2023)
Fabian Schaipp, Robert M. Gower, Michael Ulbrich
A Stochastic Proximal Polyak Step Size. CoRR (2023)
Justin Domke, Guillaume Garrigos, Robert M. Gower
Provable convergence guarantees for black-box variational inference. CoRR (2023)
Guillaume Garrigos, Robert M. Gower, Fabian Schaipp
Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM. CoRR (2023)
Bo Zhao, Robert M. Gower, Robin Walters, Rose Yu
Improving Convergence and Generalization Using Parameter Symmetries. CoRR (2023)
Si Yi Meng, Robert M. Gower
A Model-Based Method for Minimizing CVaR and Beyond. CoRR (2023)
Fabian Schaipp, Robert M. Gower, Michael Ulbrich
A Stochastic Proximal Polyak Step Size. Trans. Mach. Learn. Res. 2023 (2023)
Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower
SP2 : A Second Order Stochastic Polyak Method. ICLR (2023)
Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov, Robert M. Gower, Martin Takác
SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms. CoRR (2023)
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) (2022)
Rui Yuan, Simon S. Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. CoRR (2022)
Rui Yuan, Robert M. Gower, Alessandro Lazaric
A general sample complexity analysis of vanilla policy gradient. AISTATS (2022)
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) (2022)
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 (2022)
Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou, Fabian Pedregosa
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes. CoRR (2022)
Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower
SP2: A Second Order Stochastic Polyak Method. CoRR (2022)
Jiabin Chen, Rui Yuan, Guillaume Garrigos, Robert M. Gower
SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums. AISTATS (2022)
Rui Yuan, Alessandro Lazaric, Robert M. Gower
Sketched Newton-Raphson. SIAM J. Optim. 32 (3) (2022)
Robert M. Gower, Aaron Defazio, Michael G. Rabbat
Stochastic Polyak Stepsize with a Moving Target. CoRR (2021)
Robert M. Gower, Othmane Sebbouh, Nicolas Loizou
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. AISTATS (2021)
Aaron Defazio, Robert M. Gower
The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization. ACML (2021)
Jiabin Chen, Rui Yuan, Guillaume Garrigos, Robert M. Gower
SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums. CoRR (2021)
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) (2021)
Othmane Sebbouh, Robert M. Gower, Aaron Defazio
Almost sure convergence rates for Stochastic Gradient Descent and Stochastic Heavy Ball. COLT (2021)
Robert M. Gower, Peter Richtárik, Francis R. Bach
Stochastic quasi-gradient methods: variance reduction via Jacobian sketching. Math. Program. 188 (1) (2021)
Rui Yuan, Robert M. Gower, Alessandro Lazaric
A general sample complexity analysis of vanilla policy gradient. CoRR (2021)
Othmane Sebbouh, Robert M. Gower, Aaron Defazio
On the convergence of the Stochastic Heavy Ball Method. CoRR (2020)
Dmitry Kovalev, Robert M. Gower, Peter Richtárik, Alexander Rogozin
Fast Linear Convergence of Randomized BFGS. CoRR (2020)
Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, Peter Richtárik
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. CoRR (2020)
Rui Yuan, Alessandro Lazaric, Robert M. Gower
Sketched Newton-Raphson. CoRR (2020)
Robert M. Gower, Othmane Sebbouh, Nicolas Loizou
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. CoRR (2020)
Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik
Variance-Reduced Methods for Machine Learning. CoRR (2020)
Robert M. Gower, Margarida P. Mello
A new framework for the computation of Hessians. CoRR (2020)
Aaron Defazio, Robert M. Gower
Factorial Powers for Stochastic Optimization. CoRR (2020)
Robert M. Gower, Mark Schmidt, Francis R. Bach, Peter Richtárik
Variance-Reduced Methods for Machine Learning. Proc. IEEE 108 (11) (2020)
Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis R. Bach, Robert M. Gower
Towards closing the gap between the theory and practice of SVRG. CoRR (2019)
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)
Nidham Gazagnadou, Robert M. Gower, Joseph Salmon
Optimal mini-batch and step sizes for SAGA. CoRR (2019)
Nidham Gazagnadou, Robert M. Gower, Joseph Salmon
Optimal Mini-Batch and Step Sizes for SAGA. ICML (2019)
Robert M. Gower, Denali Molitor, Jacob D. Moorman, Deanna Needell
Adaptive Sketch-and-Project Methods for Solving Linear Systems. CoRR (2019)
Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin
SGD with Arbitrary Sampling: General Analysis and Improved Rates. ICML (2019)
Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik
RSN: Randomized Subspace Newton. NeurIPS (2019)
Brahim Khalil Abid, Robert M. Gower
Greedy stochastic algorithms for entropy-regularized optimal transport problems. CoRR (2018)
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)
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)
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 (2018)
Brahim Khalil Abid, Robert M. Gower
Stochastic algorithms for entropy-regularized optimal transport problems. AISTATS (2018)
Robert M. Gower, Peter Richtárik
Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms. SIAM J. Matrix Anal. Appl. 38 (4) (2017)
Robert M. Gower, Nicolas Le Roux, Francis R. Bach
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. CoRR (2017)
Robert M. Gower, Peter Richtárik
Randomized Quasi-Newton Updates are Linearly Convergent Matrix Inversion Algorithms. CoRR (2016)
Robert M. Gower, Donald Goldfarb, Peter Richtárik
Stochastic Block BFGS: Squeezing More Curvature out of Data. ICML (2016)
Robert M. Gower, Margarida P. Mello
A new framework for the computation of Hessians. Optim. Methods Softw. 27 (2) (2012)