A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training. - Aaron Defazio; Tibério S. Caetano (ICML, 2012)

A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation. - Aaron Defazio; Tibério S. Caetano (NIPS, 2012)

Finito: A faster, permutable incremental gradient method for big data problems. - Aaron Defazio; Justin Domke; Tibério S. Caetano (ICML, 2014)

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. - Aaron Defazio; Francis R. Bach; Simon Lacoste-Julien (NIPS, 2014)

A Comparison of learning algorithms on the Arcade Learning Environment. - Aaron Defazio; Thore Graepel (CoRR, 2014)

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. - Mark Schmidt 001; Reza Babanezhad 001; Mohamed Osama Ahmed; Aaron Defazio; Ann Clifton; Anoop Sarkar (AISTATS, 2015)

New Optimisation Methods for Machine Learning. - Aaron Defazio (CoRR, 2015)

A Simple Practical Accelerated Method for Finite Sums. - Aaron Defazio (NIPS, 2016)

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. - Jure Zbontar; Florian Knoll; Anuroop Sriram; Matthew J. Muckley; Mary Bruno; Aaron Defazio; Marc Parente; Krzysztof J. Geras; Joe Katsnelson; Hersh Chandarana; Zizhao Zhang; Michal Drozdzal; et al. (CoRR, 2018)

Controlling Covariate Shift using Equilibrium Normalization of Weights. - Aaron Defazio; Léon Bottou (CoRR, 2018)

On the Curved Geometry of Accelerated Optimization. - Aaron Defazio (NeurIPS, 2019)

On the Ineffectiveness of Variance Reduced Optimization for Deep Learning. - Aaron Defazio; Léon Bottou (NeurIPS, 2019)

Scaling Laws for the Principled Design, Initialization and Preconditioning of ReLU Networks. - Aaron Defazio; Léon Bottou (CoRR, 2019)

Offset Masking Improves Deep Learning based Accelerated MRI Reconstructions. - Aaron Defazio (CoRR, 2019)

GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction. - Anuroop Sriram; Jure Zbontar; Tullie Murrell; C. Lawrence Zitnick; Aaron Defazio; Daniel K. Sodickson (CVPR, 2020)

End-to-End Variational Networks for Accelerated MRI Reconstruction. - Anuroop Sriram; Jure Zbontar; Tullie Murrell; Aaron Defazio; C. Lawrence Zitnick; Nafissa Yakubova; Florian Knoll; Patricia M. Johnson (MICCAI (2), 2020)

MRI Banding Removal via Adversarial Training. - Aaron Defazio; Tullie Murrell; Michael P. Recht (NeurIPS, 2020)

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. - Florian Knoll; Tullie Murrell; Anuroop Sriram; Nafissa Yakubova; Jure Zbontar; Michael G. Rabbat; Aaron Defazio; Matthew J. Muckley; Daniel K. Sodickson; C. Lawrence Zitnick; Michael P. Recht (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)

Understanding the Role of Momentum in Non-Convex Optimization: Practical Insights from a Lyapunov Analysis. - Aaron Defazio (CoRR, 2020)

Dual Averaging is Surprisingly Effective for Deep Learning Optimization. - Samy Jelassi; Aaron Defazio (CoRR, 2020)

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

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

Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization. - Aaron Defazio; Samy Jelassi (CoRR, 2021)

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

Grad-GradaGrad? A Non-Monotone Adaptive Stochastic Gradient Method. - Aaron Defazio; Baoyu Zhou; Lin Xiao (CoRR, 2022)

Compressed sensing with a jackknife and a bootstrap. - Aaron Defazio; Mark Tygert; Rachel A. Ward; Jure Zbontar (J. Data Sci. Stat. Vis., 2022)

A Momentumized, Adaptive, Dual Averaged Gradient Method. - Aaron Defazio; Samy Jelassi (J. Mach. Learn. Res., 2022)

A scaling calculus for the design and initialization of ReLU networks. - Aaron Defazio; Léon Bottou (Neural Comput. Appl., 2022)

Learning-Rate-Free Learning by D-Adaptation. - Aaron Defazio; Konstantin Mishchenko (ICML, 2023)

Mechanic: A Learning Rate Tuner. - Ashok Cutkosky; Aaron Defazio; Harsh Mehta (NeurIPS, 2023)

When, Why and How Much? Adaptive Learning Rate Scheduling by Refinement. - Aaron Defazio; Ashok Cutkosky; Harsh Mehta; Konstantin Mishchenko (CoRR, 2023)

Prodigy: An Expeditiously Adaptive Parameter-Free Learner. - Konstantin Mishchenko; Aaron Defazio (ICML, 2024)

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

The Road Less Scheduled. - Aaron Defazio; Xingyu Yang; Ahmed Khaled 001; Konstantin Mishchenko; Harsh Mehta; Ashok Cutkosky (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)

PARQ: Piecewise-Affine Regularized Quantization. - Lisa Jin; Jianhao Ma; Zechun Liu; Andrey Gromov; Aaron Defazio; Lin Xiao (CoRR, 2025)

Why Gradients Rapidly Increase Near the End of Training. - Aaron Defazio (CoRR, 2025)

Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditioner. - Runa Eschenhagen; Aaron Defazio; Tsung-Hsien Lee; Richard E. Turner; Hao-Jun Michael Shi (CoRR, 2025)