Robert M. Gower

Dirk A. Lorenz

Maximilian Winkler

Comput. Optim. Appl., July, 2024

A Bregman-Kaczmarz method for nonlinear systems of equations.

[BibT_eX]

[DOI]

Dirk A. Lorenz

Maximilian Winkler

Comput. Optim. Appl., April, 2024

Enhancing Policy Gradient with the Polyak Step-Size Adaption.

[BibT_eX]

[DOI]

CoRR, 2024

Directional Smoothness and Gradient Methods: Convergence and Adaptivity.

[BibT_eX]

[DOI]

CoRR, 2024

Level Set Teleportation: An Optimization Perspective.

[BibT_eX]

[DOI]

Aaron Mishkin

Alberto Bietti

CoRR, 2024

SGD with Clipping is Secretly Estimating the Median Gradient.

[BibT_eX]

[DOI]

CoRR, 2024

MoMo: Momentum Models for Adaptive Learning Rates.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Batch and match: black-box variational inference with a score-based divergence.

[BibT_eX]

[DOI]

Diana Cai

Chirag Modi

Loucas Pillaud-Vivien

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Improving Convergence and Generalization Using Parameter Symmetries.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization.

[BibT_eX]

[DOI]

J. Optim. Theory Appl., November, 2023

A Stochastic Proximal Polyak Step Size.

[BibT_eX]

[DOI]

Fabian Schaipp

Michael Ulbrich

Trans. Mach. Learn. Res., 2023

SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms.

[BibT_eX]

[DOI]

CoRR, 2023

Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM.

[BibT_eX]

[DOI]

Guillaume Garrigos

Fabian Schaipp

CoRR, 2023

Variational Inference with Gaussian Score Matching.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Provable convergence guarantees for black-box variational inference.

[BibT_eX]

[DOI]

Justin Domke

Guillaume Garrigos

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Model-Based Method for Minimizing CVaR and Beyond.

[BibT_eX]

[DOI]

Si Yi Meng

Proceedings of the International Conference on Machine Learning, 2023

Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

SP2 : A Second Order Stochastic Polyak Method.

[BibT_eX]

[DOI]

Shuang Li

William J. Swartworth

Martin Takác

Deanna Needell

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

RidgeSketch: A Fast Sketching Based Solver for Large Scale Ridge Regression.

[BibT_eX]

[DOI]

Nidham Gazagnadou

Mark Ibrahim

SIAM J. Matrix Anal. Appl., September, 2022

A Statistical Linear Precoding Scheme Based on Random Iterative Method for Massive MIMO Systems.

[BibT_eX]

[DOI]

IEEE Trans. Wirel. Commun., 2022

Randomized Iterative Methods for Low-Complexity Large-Scale MIMO Detection.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2022

Sketched Newton-Raphson.

[BibT_eX]

[DOI]

Rui Yuan

Alessandro Lazaric

SIAM J. Optim., 2022

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes.

[BibT_eX]

[DOI]

CoRR, 2022

A general sample complexity analysis of vanilla policy gradient.

[BibT_eX]

[DOI]

Rui Yuan

Alessandro Lazaric

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

On Adaptive Sketch-and-Project for Solving Linear Systems.

[BibT_eX]

[DOI]

SIAM J. Matrix Anal. Appl., 2021

Stochastic quasi-gradient methods: variance reduction via Jacobian sketching.

[BibT_eX]

[DOI]

Francis R. Bach

Math. Program., 2021

Stochastic Polyak Stepsize with a Moving Target.

[BibT_eX]

[DOI]

Michael G. Rabbat

CoRR, 2021

Almost sure convergence rates for Stochastic Gradient Descent and Stochastic Heavy Ball.

[BibT_eX]

[DOI]

Othmane Sebbouh

Proceedings of the Conference on Learning Theory, 2021

SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation.

[BibT_eX]

[DOI]

Othmane Sebbouh

Nicolas Loizou

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization.

[BibT_eX]

[DOI]

Proceedings of the Asian Conference on Machine Learning, 2021

2020

Variance-Reduced Methods for Machine Learning.

[BibT_eX]

[DOI]

Proc. IEEE, 2020

On the convergence of the Stochastic Heavy Ball Method.

[BibT_eX]

[DOI]

Othmane Sebbouh

CoRR, 2020

Factorial Powers for Stochastic Optimization.

[BibT_eX]

[DOI]

CoRR, 2020

Fast Linear Convergence of Randomized BFGS.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Adaptive Sketch-and-Project Methods for Solving Linear Systems.

[BibT_eX]

[DOI]

CoRR, 2019

SGD: General Analysis and Improved Rates.

[BibT_eX]

[DOI]

CoRR, 2019

Towards closing the gap between the theory and practice of SVRG.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

RSN: Randomized Subspace Newton.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

SGD with Arbitrary Sampling: General Analysis and Improved Rates.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Optimal Mini-Batch and Step Sizes for SAGA.

[BibT_eX]

[DOI]

Nidham Gazagnadou

Joseph Salmon

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Greedy stochastic algorithms for entropy-regularized optimal transport problems.

[BibT_eX]

[DOI]

Brahim Khalil Abid

CoRR, 2018

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods.

[BibT_eX]

[DOI]

Nicolas Le Roux

Francis R. Bach

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Stochastic algorithms for entropy-regularized optimal transport problems.

[BibT_eX]

[DOI]

Brahim Khalil Abid

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms.

[BibT_eX]

[DOI]

SIAM J. Matrix Anal. Appl., 2017

2016

Higher-order reverse automatic differentiation with emphasis on the third-order.

[BibT_eX]

[DOI]

Artur L. Gower

Math. Program., 2016

Stochastic Block BFGS: Squeezing More Curvature out of Data.

[BibT_eX]

[DOI]

Donald Goldfarb

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Randomized Iterative Methods for Linear Systems.

[BibT_eX]

[DOI]

SIAM J. Matrix Anal. Appl., 2015

Stochastic Dual Ascent for Solving Linear Systems.

[BibT_eX]

[DOI]

CoRR, 2015

2014

Computing the sparsity pattern of Hessians using automatic differentiation.

[BibT_eX]

[DOI]

Margarida Pinheiro Mello

ACM Trans. Math. Softw., 2014

Action constrained quasi-Newton methods.

[BibT_eX]

[DOI]

Jacek Gondzio

CoRR, 2014

2012

A new framework for the computation of Hessians.

[BibT_eX]

[DOI]