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1802.03866
Cited By
Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization
12 February 2018
Zeyuan Allen-Zhu
ODL
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Papers citing
"Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization"
23 / 23 papers shown
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients
Sachin Garg
A. Berahas
Michal Dereziñski
225
2
0
23 Apr 2024
Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
Neural Information Processing Systems (NeurIPS), 2023
Le‐Yu Chen
Boyuan Yao
Luo Luo
177
17
0
29 Jul 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis
Neural Information Processing Systems (NeurIPS), 2023
Dachao Lin
Yuze Han
Haishan Ye
Zhihua Zhang
299
15
0
15 Apr 2023
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
Neural Information Processing Systems (NeurIPS), 2022
Ali Kavis
Stratis Skoulakis
Kimon Antonakopoulos
L. Dadi
Volkan Cevher
252
19
0
03 Nov 2022
Distributionally Robust Optimization via Ball Oracle Acceleration
Neural Information Processing Systems (NeurIPS), 2022
Y. Carmon
Danielle Hausler
161
14
0
24 Mar 2022
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications
Xiang Li
Zhihua Zhang
131
4
0
05 Jan 2021
Recent Theoretical Advances in Non-Convex Optimization
Marina Danilova
Pavel Dvurechensky
Alexander Gasnikov
Eduard A. Gorbunov
Sergey Guminov
Dmitry Kamzolov
Innokentiy Shibaev
346
104
0
11 Dec 2020
Global Riemannian Acceleration in Hyperbolic and Spherical Spaces
David Martínez-Rubio
557
25
0
07 Dec 2020
Tight Lower Complexity Bounds for Strongly Convex Finite-Sum Optimization
Min Zhang
Yao Shu
Kun He
122
1
0
17 Oct 2020
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates
Neural Information Processing Systems (NeurIPS), 2020
Kaiwen Zhou
Anthony Man-Cho So
James Cheng
202
5
0
25 May 2020
Multi-consensus Decentralized Accelerated Gradient Descent
Journal of machine learning research (JMLR), 2020
Haishan Ye
Luo Luo
Ziang Zhou
Tong Zhang
167
55
0
02 May 2020
Variance Reduction with Sparse Gradients
International Conference on Learning Representations (ICLR), 2020
Melih Elibol
Lihua Lei
Sai Li
111
24
0
27 Jan 2020
The Practicality of Stochastic Optimization in Imaging Inverse Problems
IEEE Transactions on Computational Imaging (TCI), 2019
Junqi Tang
K. Egiazarian
Mohammad Golbabaee
Mike Davies
280
33
0
22 Oct 2019
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization
Guangzeng Xie
Luo Luo
Zhihua Zhang
170
4
0
22 Aug 2019
ADASS: Adaptive Sample Selection for Training Acceleration
Shen-Yi Zhao
Hao Gao
Wu-Jun Li
165
0
0
11 Jun 2019
On the Convergence of Memory-Based Distributed SGD
Shen-Yi Zhao
Hao Gao
Wu-Jun Li
62
1
0
30 May 2019
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time
Annual Conference Computational Learning Theory (COLT), 2019
Y. Lee
Zhao Song
Qiuyi Zhang
247
125
0
11 May 2019
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
International Conference on Machine Learning (ICML), 2019
Dongruo Zhou
Quanquan Gu
190
48
0
31 Jan 2019
Stochastic Nested Variance Reduction for Nonconvex Optimization
Dongruo Zhou
Pan Xu
Quanquan Gu
247
158
0
20 Jun 2018
Neon2: Finding Local Minima via First-Order Oracles
Zeyuan Allen-Zhu
Yuanzhi Li
323
138
0
17 Nov 2017
Natasha 2: Faster Non-Convex Optimization Than SGD
Zeyuan Allen-Zhu
ODL
314
252
0
29 Aug 2017
Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter
International Conference on Machine Learning (ICML), 2017
Zeyuan Allen-Zhu
466
82
0
02 Feb 2017
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods
Zeyuan Allen-Zhu
ODL
550
607
0
18 Mar 2016
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