Aaron Defazio - RankMe

57 papers A* 14A 1C 1Journal 37Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	PARQ: Piecewise-Affine Regularized Quantization. ICML Lisa Jin, Jianhao Ma, Zechun Liu, Andrey Gromov, Aaron Defazio, Lin Xiao
2025	J	`jnl`	PARQ: Piecewise-Affine Regularized Quantization. CoRR Lisa Jin, Jianhao Ma, Zechun Liu, Andrey Gromov, Aaron Defazio, Lin Xiao
2025	J	`jnl`	Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditioner. CoRR Runa Eschenhagen, Aaron Defazio, Tsung-Hsien Lee, Richard E. Turner, Hao-Jun Michael Shi
2025	J	`jnl`	Smoothing DiLoCo with Primal Averaging for Faster Training of LLMs. CoRR Aaron Defazio, Konstantin Mishchenko, Parameswaran Raman, Hao-Jun Michael Shi, Lin Xiao
2025	J	`jnl`	Why Gradients Rapidly Increase Near the End of Training. CoRR Aaron Defazio
2024	A*	`conf`	Directional Smoothness and Gradient Methods: Convergence and Adaptivity. NeurIPS Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower
2024	J	`jnl`	Directional Smoothness and Gradient Methods: Convergence and Adaptivity. CoRR Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower
2024	A*	`conf`	MoMo: Momentum Models for Adaptive Learning Rates. ICML Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
2024	A*	`conf`	Prodigy: An Expeditiously Adaptive Parameter-Free Learner. ICML Konstantin Mishchenko, Aaron Defazio
2024	A*	`conf`	The Road Less Scheduled. NeurIPS Aaron Defazio, Xingyu Yang, Ahmed Khaled, Konstantin Mishchenko, Harsh Mehta, Ashok Cutkosky
2024	J	`jnl`	The Road Less Scheduled. CoRR Aaron Defazio, Xingyu Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled, Ashok Cutkosky
2023	A*	`conf`	Learning-Rate-Free Learning by D-Adaptation. ICML Aaron Defazio, Konstantin Mishchenko
2023	J	`jnl`	Learning-Rate-Free Learning by D-Adaptation. CoRR Aaron Defazio, Konstantin Mishchenko
2023	A*	`conf`	Mechanic: A Learning Rate Tuner. NeurIPS Ashok Cutkosky, Aaron Defazio, Harsh Mehta
2023	J	`jnl`	Mechanic: A Learning Rate Tuner. CoRR Ashok Cutkosky, Aaron Defazio, Harsh Mehta
2023	J	`jnl`	MoMo: Momentum Models for Adaptive Learning Rates. CoRR Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
2023	J	`jnl`	Prodigy: An Expeditiously Adaptive Parameter-Free Learner. CoRR Konstantin Mishchenko, Aaron Defazio
2023	J	`jnl`	When, Why and How Much? Adaptive Learning Rate Scheduling by Refinement. CoRR Aaron Defazio, Ashok Cutkosky, Harsh Mehta, Konstantin Mishchenko
2022	J	`jnl`	A Momentumized, Adaptive, Dual Averaged Gradient Method. J. Mach. Learn. Res. Aaron Defazio, Samy Jelassi
2022	J	`jnl`	A scaling calculus for the design and initialization of ReLU networks. Neural Comput. Appl. Aaron Defazio, Léon Bottou
2022	J	`jnl`	Compressed sensing with a jackknife and a bootstrap. J. Data Sci. Stat. Vis. Aaron Defazio, Mark Tygert, Rachel A. Ward, Jure Zbontar
2022	J	`jnl`	Grad-GradaGrad? A Non-Monotone Adaptive Stochastic Gradient Method. CoRR Aaron Defazio, Baoyu Zhou, Lin Xiao
2021	J	`jnl`	Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization. CoRR Aaron Defazio, Samy Jelassi
2021	A*	`conf`	Almost sure convergence rates for Stochastic Gradient Descent and Stochastic Heavy Ball. COLT Othmane Sebbouh, Robert M. Gower, Aaron Defazio
2021	J	`jnl`	Stochastic Polyak Stepsize with a Moving Target. CoRR Robert M. Gower, Aaron Defazio, Michael G. Rabbat
2021	C	`conf`	The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization. ACML Aaron Defazio, Robert M. Gower
2020	J	`jnl`	Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. CoRR 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
2020	J	`jnl`	Dual Averaging is Surprisingly Effective for Deep Learning Optimization. CoRR Samy Jelassi, Aaron Defazio
2020	—	`conf`	End-to-End Variational Networks for Accelerated MRI Reconstruction. MICCAI (2) Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, Patricia M. Johnson
2020	J	`jnl`	End-to-End Variational Networks for Accelerated MRI Reconstruction. CoRR Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, Patricia M. Johnson
2020	J	`jnl`	Factorial Powers for Stochastic Optimization. CoRR Aaron Defazio, Robert M. Gower
2020	A*	`conf`	GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction. CVPR Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson
2020	A*	`conf`	MRI Banding Removal via Adversarial Training. NeurIPS Aaron Defazio, Tullie Murrell, Michael P. Recht
2020	J	`jnl`	MRI Banding Removal via Adversarial Training. CoRR Aaron Defazio, Tullie Murrell, Michael P. Recht
2020	J	`jnl`	On the convergence of the Stochastic Heavy Ball Method. CoRR Othmane Sebbouh, Robert M. Gower, Aaron Defazio
2020	J	`jnl`	Understanding the Role of Momentum in Non-Convex Optimization: Practical Insights from a Lyapunov Analysis. CoRR Aaron Defazio
2019	J	`jnl`	GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction. CoRR Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson
2019	J	`jnl`	Offset Masking Improves Deep Learning based Accelerated MRI Reconstructions. CoRR Aaron Defazio
2019	A*	`conf`	On the Curved Geometry of Accelerated Optimization. NeurIPS Aaron Defazio
2019	A*	`conf`	On the Ineffectiveness of Variance Reduced Optimization for Deep Learning. NeurIPS Aaron Defazio, Léon Bottou
2019	J	`jnl`	Scaling Laws for the Principled Design, Initialization and Preconditioning of ReLU Networks. CoRR Aaron Defazio, Léon Bottou
2018	J	`jnl`	Controlling Covariate Shift using Equilibrium Normalization of Weights. CoRR Aaron Defazio, Léon Bottou
2018	J	`jnl`	On the Curved Geometry of Accelerated Optimization. CoRR Aaron Defazio
2018	J	`jnl`	On the Ineffectiveness of Variance Reduced Optimization for Deep Learning. CoRR Aaron Defazio, Léon Bottou
2018	J	`jnl`	fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. CoRR 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, Adriana Romero, Michael G. Rabbat, Pascal Vincent, James Pinkerton, Duo Wang, Nafissa Yakubova, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui
2016	—	`conf`	A Simple Practical Accelerated Method for Finite Sums. NIPS Aaron Defazio
2016	J	`jnl`	A Simple Practical Accelerated Method for Finite Sums. CoRR Aaron Defazio
2015	J	`jnl`	New Optimisation Methods for Machine Learning. CoRR Aaron Defazio
2015	A	`conf`	Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. AISTATS Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
2015	J	`jnl`	Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. CoRR Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
2014	J	`jnl`	A Comparison of learning algorithms on the Arcade Learning Environment. CoRR Aaron Defazio, Thore Graepel
2014	A*	`conf`	Finito: A faster, permutable incremental gradient method for big data problems. ICML Aaron Defazio, Justin Domke, Tibério S. Caetano
2014	—	`conf`	SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. NIPS Aaron Defazio, Francis R. Bach, Simon Lacoste-Julien
2014	J	`jnl`	SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. CoRR Aaron Defazio, Francis R. Bach, Simon Lacoste-Julien
2012	—	`conf`	A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation. NIPS Aaron Defazio, Tibério S. Caetano
2012	J	`jnl`	A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training CoRR Aaron Defazio, Tibério S. Caetano
2012	A*	`conf`	A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training. ICML Aaron Defazio, Tibério S. Caetano