A new framework for the computation of Hessians. - Robert M. Gower; Margarida P. Mello (Optim. Methods Softw., 2012)

Action constrained quasi-Newton methods. - Robert Mansel Gower; Jacek Gondzio (CoRR, 2014)

Computing the sparsity pattern of Hessians using automatic differentiation. - Robert Mansel Gower; Margarida Pinheiro Mello (ACM Trans. Math. Softw., 2014)

Stochastic Dual Ascent for Solving Linear Systems. - Robert Mansel Gower; Peter Richtárik (CoRR, 2015)

Randomized Iterative Methods for Linear Systems. - Robert Mansel Gower; Peter Richtárik (SIAM J. Matrix Anal. Appl., 2015)

Stochastic Block BFGS: Squeezing More Curvature out of Data. - Robert M. Gower; Donald Goldfarb; Peter Richtárik (ICML, 2016)

Higher-order reverse automatic differentiation with emphasis on the third-order. - Robert Mansel Gower; Artur L. Gower (Math. Program., 2016)

Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms. - Robert M. Gower; Peter Richtárik (SIAM J. Matrix Anal. Appl., 2017)

Stochastic algorithms for entropy-regularized optimal transport problems. - Brahim Khalil Abid; Robert M. Gower (AISTATS, 2018)

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. - Robert M. Gower; Nicolas Le Roux; Francis R. Bach (AISTATS, 2018)

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. - Robert M. Gower; Filip Hanzely; Peter Richtárik; Sebastian U. Stich (NeurIPS, 2018)

Greedy stochastic algorithms for entropy-regularized optimal transport problems. - Brahim Khalil Abid; Robert M. Gower (CoRR, 2018)

Optimal Mini-Batch and Step Sizes for SAGA. - Nidham Gazagnadou; Robert M. Gower; Joseph Salmon (ICML, 2019)

SGD with Arbitrary Sampling: General Analysis and Improved Rates. - Xun Qian; Peter Richtárik; Robert M. Gower; Alibek Sailanbayev; Nicolas Loizou; Egor Shulgin (ICML, 2019)

RSN: Randomized Subspace Newton. - Robert M. Gower; Dmitry Kovalev; Felix Lieder; Peter Richtárik (NeurIPS, 2019)

Towards closing the gap between the theory and practice of SVRG. - Othmane Sebbouh; Nidham Gazagnadou; Samy Jelassi; Francis R. Bach; Robert M. Gower (NeurIPS, 2019)

SGD: General Analysis and Improved Rates. - Robert Mansel Gower; Nicolas Loizou; Xun Qian; Alibek Sailanbayev; Egor Shulgin; Peter Richtárik (CoRR, 2019)

Adaptive Sketch-and-Project Methods for Solving Linear Systems. - Robert M. Gower; Denali Molitor; Jacob D. Moorman; Deanna Needell (CoRR, 2019)

Fast Linear Convergence of Randomized BFGS. - Dmitry Kovalev; Robert M. Gower; Peter Richtárik; Alexander Rogozin (CoRR, 2020)

Factorial Powers for Stochastic Optimization. - Aaron Defazio; Robert M. Gower (CoRR, 2020)

On the convergence of the Stochastic Heavy Ball Method. - Othmane Sebbouh; Robert M. Gower; Aaron Defazio (CoRR, 2020)

Variance-Reduced Methods for Machine Learning. - Robert M. Gower; Mark Schmidt 001; Francis R. Bach; Peter Richtárik (Proc. IEEE, 2020)

The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization. - Aaron Defazio; Robert M. Gower (ACML, 2021)

SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation. - Robert M. Gower; Othmane Sebbouh; Nicolas Loizou (AISTATS, 2021)

Almost sure convergence rates for Stochastic Gradient Descent and Stochastic Heavy Ball. - Othmane Sebbouh; Robert M. Gower; Aaron Defazio (COLT, 2021)

Stochastic Polyak Stepsize with a Moving Target. - Robert M. Gower; Aaron Defazio; Michael G. Rabbat (CoRR, 2021)

Stochastic quasi-gradient methods: variance reduction via Jacobian sketching. - Robert M. Gower; Peter Richtárik; Francis R. Bach (Math. Program., 2021)

On Adaptive Sketch-and-Project for Solving Linear Systems. - Robert M. Gower; Denali Molitor; Jacob D. Moorman; Deanna Needell (SIAM J. Matrix Anal. Appl., 2021)

SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums. - Jiabin Chen; Rui Yuan; Guillaume Garrigos; Robert M. Gower (AISTATS, 2022)

A general sample complexity analysis of vanilla policy gradient. - Rui Yuan; Robert M. Gower; Alessandro Lazaric (AISTATS, 2022)

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes. - Robert M. Gower; Mathieu Blondel; Nidham Gazagnadou; Fabian Pedregosa (CoRR, 2022)

Sketched Newton-Raphson. - Rui Yuan; Alessandro Lazaric; Robert M. Gower (SIAM J. Optim., 2022)

Randomized Iterative Methods for Low-Complexity Large-Scale MIMO Detection. - Zheng Wang 013; Robert M. Gower; Yili Xia; Lanxin He; Yongming Huang 001 (IEEE Trans. Signal Process., 2022)

A Statistical Linear Precoding Scheme Based on Random Iterative Method for Massive MIMO Systems. - Zheng Wang 013; Robert M. Gower; Cheng Zhang 004; Shanxiang Lyu; Yili Xia; Yongming Huang 001 (IEEE Trans. Wirel. Commun., 2022)

RidgeSketch: A Fast Sketching Based Solver for Large Scale Ridge Regression. - Nidham Gazagnadou; Mark Ibrahim; Robert M. Gower (SIAM J. Matrix Anal. Appl., 2022)

SP2 : A Second Order Stochastic Polyak Method. - Shuang Li 003; William J. Swartworth; Martin Takác 001; Deanna Needell; Robert M. Gower (ICLR, 2023)

Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. - Rui Yuan; Simon Shaolei Du; Robert M. Gower; Alessandro Lazaric; Lin Xiao (ICLR, 2023)

A Model-Based Method for Minimizing CVaR and Beyond. - Si Yi Meng; Robert M. Gower (ICML, 2023)

Provable convergence guarantees for black-box variational inference. - Justin Domke; Robert M. Gower; Guillaume Garrigos (NeurIPS, 2023)

Variational Inference with Gaussian Score Matching. - Chirag Modi 002; Robert M. Gower; Charles Margossian; Yuling Yao; David M. Blei; Lawrence K. Saul (NeurIPS, 2023)

Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM. - Guillaume Garrigos; Robert M. Gower; Fabian Schaipp (CoRR, 2023)

SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms. - Farshed Abdukhakimov; Chulu Xiang; Dmitry Kamzolov; Robert M. Gower; Martin Takác 001 (CoRR, 2023)

A Stochastic Proximal Polyak Step Size. - Fabian Schaipp; Robert M. Gower; Michael Ulbrich (Trans. Mach. Learn. Res., 2023)

Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization. - Ahmed Khaled 001; Othmane Sebbouh; Nicolas Loizou; Robert M. Gower; Peter Richtárik (J. Optim. Theory Appl., 2023)

Improving Convergence and Generalization Using Parameter Symmetries. - Bo Zhao 028; Robert M. Gower; Robin Walters 001; Rose Yu (ICLR, 2024)

Batch and match: black-box variational inference with a score-based divergence. - Diana Cai; Chirag Modi 002; Loucas Pillaud-Vivien; Charles Margossian; Robert M. Gower; David M. Blei; Lawrence K. Saul (ICML, 2024)

MoMo: Momentum Models for Adaptive Learning Rates. - Fabian Schaipp; Ruben Ohana; Michael Eickenberg; Aaron Defazio; Robert M. Gower (ICML, 2024)

EigenVI: score-based variational inference with orthogonal function expansions. - Diana Cai; Chirag Modi 002; Charles Margossian; Robert M. Gower; David M. Blei; Lawrence K. Saul (NeurIPS, 2024)

Directional Smoothness and Gradient Methods: Convergence and Adaptivity. - Aaron Mishkin; Ahmed Khaled 001; Yuanhao Wang 001; Aaron Defazio; Robert M. Gower (NeurIPS, 2024)

SGD with Clipping is Secretly Estimating the Median Gradient. - Fabian Schaipp; Guillaume Garrigos; Umut Simsekli; Robert M. Gower (CoRR, 2024)

Enhancing Policy Gradient with the Polyak Step-Size Adaption. - Yunxiang Li; Rui Yuan; Chen Fan; Mark Schmidt 001; Samuel Horváth; Robert M. Gower; Martin Takác 001 (CoRR, 2024)

A Bregman-Kaczmarz method for nonlinear systems of equations. - Robert M. Gower; Dirk A. Lorenz; Maximilian Winkler (Comput. Optim. Appl., 2024)

Correction: A Bregman-Kaczmarz method for nonlinear systems of equations. - Robert Mansel Gower; Dirk A. Lorenz; Maximilian Winkler (Comput. Optim. Appl., 2024)

Level Set Teleportation: An Optimization Perspective. - Aaron Mishkin; Alberto Bietti; Robert M. Gower (AISTATS, 2025)

Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation. - Robert M. Gower; Guillaume Garrigos; Nicolas Loizou; Dimitris Oikonomou; Konstantin Mishchenko; Fabian Schaipp (CoRR, 2025)