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Learning-Rate-Free Learning by D-Adaptation
v1v2v3v4v5 (latest)

Learning-Rate-Free Learning by D-Adaptation

International Conference on Machine Learning (ICML), 2023
18 January 2023
Aaron Defazio
Konstantin Mishchenko
ArXiv (abs)PDFHTML

Papers citing "Learning-Rate-Free Learning by D-Adaptation"

50 / 78 papers shown
Title
Deep Progressive Training: scaling up depth capacity of zero/one-layer models
Deep Progressive Training: scaling up depth capacity of zero/one-layer models
Zhiqi Bu
AI4CE
124
0
0
07 Nov 2025
ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push
ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push
Xiaoming Wu
Teng Liu
Xin Wang
Ming Yang
Jiguo Yu
96
0
0
23 Oct 2025
Convergence of Regret Matching in Potential Games and Constrained Optimization
Convergence of Regret Matching in Potential Games and Constrained Optimization
Ioannis Anagnostides
Emanuel Tewolde
B. Zhang
Ioannis Panageas
Vincent Conitzer
Tuomas Sandholm
196
1
0
20 Oct 2025
AutoGD: Automatic Learning Rate Selection for Gradient Descent
AutoGD: Automatic Learning Rate Selection for Gradient Descent
Nikola Surjanovic
Alexandre Bouchard-Côté
Trevor Campbell
ODL
221
0
0
10 Oct 2025
Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Kristi Topollai
A. Choromańska
ODL
307
0
0
06 Oct 2025
Non-Euclidean Broximal Point Method: A Blueprint for Geometry-Aware Optimization
Non-Euclidean Broximal Point Method: A Blueprint for Geometry-Aware Optimization
Kaja Gruntkowska
Peter Richtárik
156
2
0
01 Oct 2025
A regret minimization approach to fixed-point iterations
A regret minimization approach to fixed-point iterations
Joon Kwon
112
0
0
25 Sep 2025
Development of Deep Learning Optimizers: Approaches, Concepts, and Update Rules
Development of Deep Learning Optimizers: Approaches, Concepts, and Update Rules
Doğay Altınel
116
0
0
22 Sep 2025
Stochastic Adaptive Gradient Descent Without Descent
Stochastic Adaptive Gradient Descent Without Descent
Jean-François Aujol
Jérémie Bigot
Camille Castera
ODL
228
1
0
18 Sep 2025
Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam
Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam
Asma Atamna
Tom Maus
Fabian Kievelitz
Tobias Glasmachers
56
0
0
07 Aug 2025
Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization
Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization
Ekaterina Borodich
D. Kovalev
136
1
0
13 Jul 2025
Weakly Supervised Object Segmentation by Background Conditional Divergence
Weakly Supervised Object Segmentation by Background Conditional Divergence
Hassan Baker
Matthew Emigh
Austin J. Brockmeier
116
0
0
25 Jun 2025
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
Jared Lawrence
Ari Kalinsky
Hannah Bradfield
Y. Carmon
Oliver Hinder
197
0
0
12 Jun 2025
Why Gradients Rapidly Increase Near the End of Training
Why Gradients Rapidly Increase Near the End of Training
Aaron Defazio
140
6
0
02 Jun 2025
LightSAM: Parameter-Agnostic Sharpness-Aware Minimization
LightSAM: Parameter-Agnostic Sharpness-Aware Minimization
Yifei Cheng
Li Shen
Hao Sun
Nan Yin
Xiaochun Cao
Enhong Chen
AAML
221
0
0
30 May 2025
How far away are truly hyperparameter-free learning algorithms?
How far away are truly hyperparameter-free learning algorithms?
Priya Kasimbeg
Vincent Roulet
Naman Agarwal
Sourabh Medapati
Fabian Pedregosa
Atish Agarwala
George E. Dahl
151
0
0
29 May 2025
AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent
AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent
Nikola Surjanovic
Alexandre Bouchard-Côté
Trevor Campbell
134
1
0
27 May 2025
Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation
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
249
5
0
02 Apr 2025
Benefits of Learning Rate Annealing for Tuning-Robustness in Stochastic Optimization
Amit Attia
Tomer Koren
361
2
0
13 Mar 2025
Towards hyperparameter-free optimization with differential privacyInternational Conference on Learning Representations (ICLR), 2025
Zhiqi Bu
Ruixuan Liu
242
7
0
02 Mar 2025
LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data
LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data
Peer Nagy
Sascha Frey
Kang Li
Bidipta Sarkar
Svitlana Vyetrenko
Stefan Zohren
Ani Calinescu
Jakob Foerster
608
4
0
13 Feb 2025
A Hessian-informed hyperparameter optimization for differential learning rate
A Hessian-informed hyperparameter optimization for differential learning rate
Shiyun Xu
Zhiqi Bu
Yiliang Zhang
Ian Barnett
338
1
0
12 Jan 2025
Temporal Context Consistency Above All: Enhancing Long-Term Anticipation
  by Learning and Enforcing Temporal Constraints
Temporal Context Consistency Above All: Enhancing Long-Term Anticipation by Learning and Enforcing Temporal Constraints
Alberto Maté
Mariella Dimiccoli
AI4TS
270
2
0
27 Dec 2024
MARINA-P: Superior Performance in Non-smooth Federated Optimization with Adaptive Stepsizes
Igor Sokolov
Peter Richtárik
303
1
0
22 Dec 2024
An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient
  Descent: Enhancing Unconstrained Optimization with VAV method
An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method
Jiahao Zhang
Christian Moya
Guang Lin
219
0
0
10 Nov 2024
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex
  Neural Networks
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural NetworksNeural Information Processing Systems (NeurIPS), 2024
Miria Feng
Zachary Frangella
Mert Pilanci
BDL
410
3
0
02 Nov 2024
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rateNorth American Chapter of the Association for Computational Linguistics (NAACL), 2024
Zhiqi Bu
Xiaomeng Jin
Bhanukiran Vinzamuri
Anil Ramakrishna
Kai-Wei Chang
Volkan Cevher
Mingyi Hong
MU
521
21
0
29 Oct 2024
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoGConference on Uncertainty in Artificial Intelligence (UAI), 2024
Naitong Chen
Jonathan H. Huggins
Trevor Campbell
293
0
0
24 Oct 2024
A second-order-like optimizer with adaptive gradient scaling for deep
  learning
A second-order-like optimizer with adaptive gradient scaling for deep learning
Jérôme Bolte
Ryan Boustany
Edouard Pauwels
Andrei Purica
ODL
182
0
0
08 Oct 2024
Diffusing to the Top: Boost Graph Neural Networks with Minimal
  Hyperparameter Tuning
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningInternational Conference on Learning Representations (ICLR), 2024
Lequan Lin
Dai Shi
Andi Han
Zhiyong Wang
Junbin Gao
272
0
0
08 Oct 2024
Old Optimizer, New Norm: An Anthology
Old Optimizer, New Norm: An Anthology
Jeremy Bernstein
Laker Newhouse
ODL
315
64
0
30 Sep 2024
State-free Reinforcement Learning
State-free Reinforcement LearningNeural Information Processing Systems (NeurIPS), 2024
Mingyu Chen
Aldo Pacchiano
Xuezhou Zhang
231
0
0
27 Sep 2024
VMAS: Video-to-Music Generation via Semantic Alignment in Web Music
  Videos
VMAS: Video-to-Music Generation via Semantic Alignment in Web Music VideosIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2024
Yan-Bo Lin
Yu Tian
L. Yang
Gedas Bertasius
Heng Wang
VGen
218
13
0
11 Sep 2024
Exploring Foundation Models for Synthetic Medical Imaging: A Study on
  Chest X-Rays and Fine-Tuning Techniques
Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques
Davide Clode da Silva
Marina Musse Bernardes
Nathalia Giacomini Ceretta
Gabriel Vaz de Souza
Gabriel Fonseca Silva
Rafael Heitor Bordini
S. Musse
MedImLM&MA
149
0
0
06 Sep 2024
Stepping on the Edge: Curvature Aware Learning Rate Tuners
Stepping on the Edge: Curvature Aware Learning Rate Tuners
Vincent Roulet
Atish Agarwala
Jean-Bastien Grill
Grzegorz Swirszcz
Mathieu Blondel
Fabian Pedregosa
326
4
0
08 Jul 2024
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton
  Stepsizes
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
Antonio Orvieto
Lin Xiao
282
6
0
05 Jul 2024
Dreamguider: Improved Training free Diffusion-based Conditional
  Generation
Dreamguider: Improved Training free Diffusion-based Conditional Generation
Nithin Gopalakrishnan Nair
Vishal M. Patel
273
3
0
04 Jun 2024
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
Daniel Dodd
Louis Sharrock
Christopher Nemeth
252
1
0
04 Jun 2024
Fully Unconstrained Online Learning
Fully Unconstrained Online Learning
Ashok Cutkosky
Zakaria Mhammedi
CLL
155
5
0
30 May 2024
The High Line: Exact Risk and Learning Rate Curves of Stochastic
  Adaptive Learning Rate Algorithms
The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
Elizabeth Collins-Woodfin
Inbar Seroussi
Begona García Malaxechebarría
Andrew W. Mackenzie
Elliot Paquette
Courtney Paquette
159
2
0
30 May 2024
The Road Less Scheduled
The Road Less Scheduled
Aaron Defazio
Xingyu Yang
Yang
Harsh Mehta
Konstantin Mishchenko
Ahmed Khaled
Ashok Cutkosky
406
117
0
24 May 2024
Scalable Optimization in the Modular Norm
Scalable Optimization in the Modular NormNeural Information Processing Systems (NeurIPS), 2024
Tim Large
Yang Liu
Minyoung Huh
Hyojin Bahng
Phillip Isola
Jeremy Bernstein
237
30
0
23 May 2024
Unleash Graph Neural Networks from Heavy Tuning
Unleash Graph Neural Networks from Heavy Tuning
Lequan Lin
Dai Shi
Andi Han
Zhiyong Wang
Junbin Gao
AI4CE
218
3
0
21 May 2024
Towards Stability of Parameter-free Optimization
Towards Stability of Parameter-free Optimization
Yijiang Pang
Shuyang Yu
Hoang Bao
Jiayu Zhou
341
1
0
07 May 2024
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
Sayantan Choudhury
N. Tupitsa
Nicolas Loizou
Samuel Horváth
Martin Takáč
Eduard A. Gorbunov
385
5
0
05 Mar 2024
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
Wuyang Chen
Jialin Song
Pu Ren
Shashank Subramanian
Dmitriy Morozov
Michael W. Mahoney
AI4CE
412
20
0
24 Feb 2024
Tuning-Free Stochastic Optimization
Tuning-Free Stochastic Optimization
Ahmed Khaled
Chi Jin
226
13
0
12 Feb 2024
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
P. Ostroukhov
Aigerim Zhumabayeva
Chulu Xiang
Alexander Gasnikov
Martin Takáč
Dmitry Kamzolov
ODL
202
2
0
07 Feb 2024
How Free is Parameter-Free Stochastic Optimization?
How Free is Parameter-Free Stochastic Optimization?International Conference on Machine Learning (ICML), 2024
Amit Attia
Tomer Koren
ODL
332
10
0
05 Feb 2024
MetaOptimize: A Framework for Optimizing Step Sizes and Other
  Meta-parameters
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
Arsalan Sharifnassab
Saber Salehkaleybar
Richard Sutton
353
3
0
04 Feb 2024
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