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2012.05640
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Asymptotic study of stochastic adaptive algorithm in non-convex landscape
Journal of machine learning research (JMLR), 2020
10 December 2020
S. Gadat
Ioana Gavra
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Papers citing
"Asymptotic study of stochastic adaptive algorithm in non-convex landscape"
15 / 15 papers shown
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Why Transformers Need Adam: A Hessian Perspective
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On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations
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Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case
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20 Jul 2023
Convergence of AdaGrad for Non-convex Objectives: Simple Proofs and Relaxed Assumptions
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29 May 2023
Convergence of Adam Under Relaxed Assumptions
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Alexander Rakhlin
Ali Jadbabaie
563
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27 Apr 2023
Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications
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Pierre Tarrago
200
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Provable Adaptivity of Adam under Non-uniform Smoothness
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Bohan Wang
Yushun Zhang
Huishuai Zhang
Qi Meng
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Zhirui Ma
Tie-Yan Liu
Zhimin Luo
Wei Chen
265
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Adam Can Converge Without Any Modification On Update Rules
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Yushun Zhang
Congliang Chen
Naichen Shi
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Zhimin Luo
567
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The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
Annual Conference Computational Learning Theory (COLT), 2022
Matthew Faw
Isidoros Tziotis
Constantine Caramanis
Aryan Mokhtari
Sanjay Shakkottai
Rachel A. Ward
266
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11 Feb 2022
On the Convergence of mSGD and AdaGrad for Stochastic Optimization
International Conference on Learning Representations (ICLR), 2022
Ruinan Jin
Yu Xing
Xingkang He
172
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26 Jan 2022
A theoretical and empirical study of new adaptive algorithms with additional momentum steps and shifted updates for stochastic non-convex optimization
C. Alecsa
252
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16 Oct 2021
Stochastic Subgradient Descent on a Generic Definable Function Converges to a Minimizer
S. Schechtman
310
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06 Sep 2021
Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance
Anas Barakat
Pascal Bianchi
W. Hachem
S. Schechtman
354
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07 Dec 2020
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