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引用“”的文章Stochastic heavy ball

标题/作者	年份
Momentum and stochastic momentum for stochastic gradient, newton, proximal point and subspace descent methods N Loizou, P Richtárik Computational Optimization and Applications 77 (3), 653-710	2020
Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration S De, A Mukherjee, E Ullah arXiv preprint arXiv:1807.06766	2018
Understanding the role of momentum in stochastic gradient methods I Gitman, H Lang, P Zhang, L Xiao Advances in Neural Information Processing Systems 32	2019
Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball O Sebbouh, RM Gower, A Defazio Conference on Learning Theory, 3935-3971	2021
Optimal non-asymptotic bound of the Ruppert-Polyak averaging without strong convexity S Gadat, F Panloup arXiv preprint arXiv:1709.03342	2017
Convergence and dynamical behavior of the ADAM algorithm for nonconvex stochastic optimization A Barakat, P Bianchi SIAM Journal on Optimization 31 (1), 244-274	2021
Convergence of a stochastic gradient method with momentum for non-smooth non-convex optimization V Mai, M Johansson International conference on machine learning, 6630-6639	2020
Linearly convergent stochastic heavy ball method for minimizing generalization error N Loizou, P Richtárik arXiv preprint arXiv:1710.10737	2017
Optimization with momentum: Dynamical, control-theoretic, and symplectic perspectives M Muehlebach, MI Jordan The Journal of Machine Learning Research 22 (1), 3407-3456	2021
A better alternative to error feedback for communication-efficient distributed learning S Horváth, P Richtárik arXiv preprint arXiv:2006.11077	2020
Anderson acceleration of proximal gradient methods V Mai, M Johansson International Conference on Machine Learning, 6620-6629	2020
A high probability analysis of adaptive sgd with momentum X Li, F Orabona arXiv preprint arXiv:2007.14294	2020
Accelerated linear convergence of stochastic momentum methods in wasserstein distances B Can, M Gurbuzbalaban, L Zhu International Conference on Machine Learning, 891-901	2019
A general system of differential equations to model first-order adaptive algorithms AB Da Silva, M Gazeau The Journal of Machine Learning Research 21 (1), 5072-5113	2020
Continuous time analysis of momentum methods NB Kovachki, AM Stuart The Journal of Machine Learning Research 22 (1), 760-799	2021
On the rates of convergence of parallelized averaged stochastic gradient algorithms A Godichon-Baggioni, S Saadane Statistics 54 (3), 618-635	2020
On the generalization of stochastic gradient descent with momentum A Ramezani-Kebrya, A Khisti, B Liang arXiv preprint arXiv:1809.04564	2018
A modular analysis of provable acceleration via polyak’s momentum: Training a wide relu network and a deep linear network JK Wang, CH Lin, JD Abernethy International Conference on Machine Learning, 10816-10827	2021
An accelerated dfo algorithm for finite-sum convex functions Y Chen, A Orvieto, A Lucchi arXiv preprint arXiv:2007.03311	2020
Convergence and dynamical behavior of the Adam algorithm for non-convex stochastic optimization A Barakat, P Bianchi arXiv preprint arXiv:1810.02263	2018

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