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  4. Cited By
Less Regret via Online Conditioning

Less Regret via Online Conditioning

25 February 2010
Matthew J. Streeter
H. B. McMahan
    ODL
ArXiv (abs)PDFHTML

Papers citing "Less Regret via Online Conditioning"

50 / 50 papers shown
Improved Convergence in Parameter-Agnostic Error Feedback through Momentum
Improved Convergence in Parameter-Agnostic Error Feedback through Momentum
Abdurakhmon Sadiev
Yury Demidovich
Igor Sokolov
Grigory Malinovsky
Sarit Khirirat
Peter Richtárik
82
0
0
18 Nov 2025
AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates
AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates
Minxin Zhang
Yuxuan Liu
Hayden Schaeffer
191
4
0
03 Sep 2025
ASGO: Adaptive Structured Gradient Optimization
ASGO: Adaptive Structured Gradient Optimization
Kang An
Yuxing Liu
Boyao Wang
Shiqian Ma
Shiqian Ma
Tong Zhang
Tong Zhang
ODL
430
26
0
26 Mar 2025
Structured Preconditioners in Adaptive Optimization: A Unified Analysis
Structured Preconditioners in Adaptive Optimization: A Unified Analysis
Shuo Xie
Tianhao Wang
Sashank J. Reddi
Sanjiv Kumar
Zhiyuan Li
274
14
0
13 Mar 2025
Bandit and Delayed Feedback in Online Structured Prediction
Bandit and Delayed Feedback in Online Structured Prediction
Yuki Shibukawa
Taira Tsuchiya
Shinsaku Sakaue
Kenji Yamanishi
OffRL
327
1
0
26 Feb 2025
Efficiently Solving Discounted MDPs with Predictions on Transition Matrices
Efficiently Solving Discounted MDPs with Predictions on Transition Matrices
Lixing Lyu
Jiashuo Jiang
Wang Chi Cheung
275
3
0
24 Feb 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
293
2
0
27 Dec 2024
Tilted Sharpness-Aware Minimization
Tilted Sharpness-Aware Minimization
Tian Li
Wanrong Zhu
J. Bilmes
313
0
0
30 Oct 2024
Large Batch Analysis for Adagrad Under Anisotropic Smoothness
Large Batch Analysis for Adagrad Under Anisotropic Smoothness
Yuxing Liu
Boyao Wang
Tong Zhang
256
0
0
21 Jun 2024
Low-Resource Machine Translation through the Lens of Personalized
  Federated Learning
Low-Resource Machine Translation through the Lens of Personalized Federated LearningConference on Empirical Methods in Natural Language Processing (EMNLP), 2024
Viktor Moskvoretskii
N. Tupitsa
Chris Biemann
Samuel Horváth
Eduard A. Gorbunov
Irina Nikishina
FedML
186
1
0
18 Jun 2024
An Equivalence Between Static and Dynamic Regret Minimization
An Equivalence Between Static and Dynamic Regret Minimization
Andrew Jacobsen
Francesco Orabona
254
5
0
03 Jun 2024
Directional Smoothness and Gradient Methods: Convergence and Adaptivity
Directional Smoothness and Gradient Methods: Convergence and Adaptivity
Aaron Mishkin
Ahmed Khaled
Yuanhao Wang
Aaron Defazio
Robert Mansel Gower
423
16
0
06 Mar 2024
Revisiting Convergence of AdaGrad with Relaxed Assumptions
Revisiting Convergence of AdaGrad with Relaxed Assumptions
Yusu Hong
Junhong Lin
275
13
0
21 Feb 2024
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
Tim Tsz-Kit Lau
Han Liu
Mladen Kolar
ODL
382
9
0
17 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
206
2
0
07 Feb 2024
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
On Convergence of Adam for Stochastic Optimization under Relaxed AssumptionsNeural Information Processing Systems (NeurIPS), 2024
Yusu Hong
Junhong Lin
405
17
0
06 Feb 2024
Parameter-Agnostic Optimization under Relaxed Smoothness
Parameter-Agnostic Optimization under Relaxed SmoothnessInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Florian Hübler
Junchi Yang
Xiang Li
Niao He
262
29
0
06 Nov 2023
High Probability Convergence of Adam Under Unbounded Gradients and
  Affine Variance Noise
High Probability Convergence of Adam Under Unbounded Gradients and Affine Variance Noise
Yusu Hong
Junhong Lin
212
11
0
03 Nov 2023
Improved Algorithms for Adversarial Bandits with Unbounded Losses
Improved Algorithms for Adversarial Bandits with Unbounded Losses
Mingyu Chen
Xuezhou Zhang
218
3
0
03 Oct 2023
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence
  and Variance Reduction
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance ReductionNeural Information Processing Systems (NeurIPS), 2023
Xiao-Yan Jiang
Sebastian U. Stich
242
30
0
11 Aug 2023
Normalized Gradients for All
Normalized Gradients for All
Francesco Orabona
206
18
0
10 Aug 2023
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex
  Optimization
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex OptimizationNeural Information Processing Systems (NeurIPS), 2023
Massil Hihat
Stéphane Gaïffas
Guillaume Garrigos
Simon Bussy
150
3
0
12 Jul 2023
Prodigy: An Expeditiously Adaptive Parameter-Free Learner
Prodigy: An Expeditiously Adaptive Parameter-Free LearnerInternational Conference on Machine Learning (ICML), 2023
Konstantin Mishchenko
Aaron Defazio
ODL
420
106
0
09 Jun 2023
Generalized Implicit Follow-The-Regularized-Leader
Generalized Implicit Follow-The-Regularized-LeaderInternational Conference on Machine Learning (ICML), 2023
Keyi Chen
Francesco Orabona
FedML
171
3
0
31 May 2023
Parameter-free projected gradient descent
Parameter-free projected gradient descent
Evgenii Chzhen
Christophe Giraud
Jean-Michel Poggi
224
4
0
31 May 2023
SignSVRG: fixing SignSGD via variance reduction
SignSVRG: fixing SignSGD via variance reduction
Evgenii Chzhen
S. Schechtman
235
5
0
22 May 2023
Two Sides of One Coin: the Limits of Untuned SGD and the Power of
  Adaptive Methods
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive MethodsNeural Information Processing Systems (NeurIPS), 2023
Junchi Yang
Xiang Li
Ilyas Fatkhullin
Niao He
218
23
0
21 May 2023
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGDAnnual Conference Computational Learning Theory (COLT), 2023
Matthew Faw
Litu Rout
Constantine Caramanis
Sanjay Shakkottai
311
46
0
13 Feb 2023
Rethinking Warm-Starts with Predictions: Learning Predictions Close to
  Sets of Optimal Solutions for Faster $\text{L}$-/$\text{L}^\natural$-Convex
  Function Minimization
Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster L\text{L}L-/L♮\text{L}^\naturalL♮-Convex Function MinimizationInternational Conference on Machine Learning (ICML), 2023
Shinsaku Sakaue
Taihei Oki
192
2
0
02 Feb 2023
Learning-Rate-Free Learning by D-Adaptation
Learning-Rate-Free Learning by D-AdaptationInternational Conference on Machine Learning (ICML), 2023
Aaron Defazio
Konstantin Mishchenko
476
107
0
18 Jan 2023
Multi-Agent Reinforcement Learning with Reward Delays
Multi-Agent Reinforcement Learning with Reward DelaysConference on Learning for Dynamics & Control (L4DC), 2022
Yuyang Zhang
Runyu Zhang
Yu Gu
Na Li
243
13
0
02 Dec 2022
Differentially Private Adaptive Optimization with Delayed
  Preconditioners
Differentially Private Adaptive Optimization with Delayed PreconditionersInternational Conference on Learning Representations (ICLR), 2022
Tian Li
Manzil Zaheer
Ziyu Liu
Sashank J. Reddi
H. B. McMahan
Virginia Smith
253
15
0
01 Dec 2022
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum
  Minimization
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum MinimizationNeural Information Processing Systems (NeurIPS), 2022
Ali Kavis
Stratis Skoulakis
Kimon Antonakopoulos
L. Dadi
Volkan Cevher
257
19
0
03 Nov 2022
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax
  Optimization
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax OptimizationNeural Information Processing Systems (NeurIPS), 2022
Junchi Yang
Xiang Li
Niao He
ODL
259
25
0
01 Jun 2022
Convergence of First-Order Methods for Constrained Nonconvex
  Optimization with Dependent Data
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent DataInternational Conference on Machine Learning (ICML), 2022
Ahmet Alacaoglu
Hanbaek Lyu
267
5
0
29 Mar 2022
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded
  Gradients and Affine Variance
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine VarianceAnnual Conference Computational Learning Theory (COLT), 2022
Matthew Faw
Isidoros Tziotis
Constantine Caramanis
Aryan Mokhtari
Sanjay Shakkottai
Rachel A. Ward
229
69
0
11 Feb 2022
Online Learning to Transport via the Minimal Selection Principle
Online Learning to Transport via the Minimal Selection PrincipleAnnual Conference Computational Learning Theory (COLT), 2022
Wenxuan Guo
Y. Hur
Tengyuan Liang
Christopher Ryan
185
6
0
09 Feb 2022
Cooperative Online Learning in Stochastic and Adversarial MDPs
Cooperative Online Learning in Stochastic and Adversarial MDPsInternational Conference on Machine Learning (ICML), 2022
Tal Lancewicki
Aviv A. Rosenberg
Yishay Mansour
290
3
0
31 Jan 2022
On the Convergence of mSGD and AdaGrad for Stochastic Optimization
On the Convergence of mSGD and AdaGrad for Stochastic OptimizationInternational Conference on Learning Representations (ICLR), 2022
Ruinan Jin
Yu Xing
Xingkang He
124
12
0
26 Jan 2022
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants
  via the Mirror Stochastic Polyak Stepsize
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize
Ryan DÓrazio
Nicolas Loizou
I. Laradji
Ioannis Mitliagkas
422
33
0
28 Oct 2021
On the Last Iterate Convergence of Momentum Methods
On the Last Iterate Convergence of Momentum MethodsInternational Conference on Algorithmic Learning Theory (ALT), 2021
Xiaoyun Li
Mingrui Liu
Francesco Orabona
317
12
0
13 Feb 2021
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces
Ashok Cutkosky
Francesco Orabona
249
164
0
17 Feb 2018
Training Deep Networks without Learning Rates Through Coin Betting
Training Deep Networks without Learning Rates Through Coin Betting
Francesco Orabona
Tatiana Tommasi
ODL
237
4
0
22 May 2017
Scale-Free Online Learning
Scale-Free Online Learning
Francesco Orabona
D. Pál
255
114
0
08 Jan 2016
Scale-Free Algorithms for Online Linear Optimization
Scale-Free Algorithms for Online Linear Optimization
Francesco Orabona
D. Pál
ODL
206
55
0
19 Feb 2015
Simultaneous Model Selection and Optimization through Parameter-free
  Stochastic Learning
Simultaneous Model Selection and Optimization through Parameter-free Stochastic LearningNeural Information Processing Systems (NeurIPS), 2014
Francesco Orabona
282
106
0
15 Jun 2014
A Survey of Algorithms and Analysis for Adaptive Online Learning
A Survey of Algorithms and Analysis for Adaptive Online Learning
H. B. McMahan
FedML
262
17
0
14 Mar 2014
Large-Scale Learning with Less RAM via Randomization
Large-Scale Learning with Less RAM via RandomizationInternational Conference on Machine Learning (ICML), 2013
Daniel Golovin
D. Sculley
H. B. McMahan
Michael Young
101
25
0
19 Mar 2013
A Unified View of Regularized Dual Averaging and Mirror Descent with
  Implicit Updates
A Unified View of Regularized Dual Averaging and Mirror Descent with Implicit Updates
H. B. McMahan
249
33
0
16 Sep 2010
Adaptive Bound Optimization for Online Convex Optimization
Adaptive Bound Optimization for Online Convex OptimizationAnnual Conference Computational Learning Theory (COLT), 2010
H. B. McMahan
Matthew J. Streeter
ODL
342
411
0
26 Feb 2010
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