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Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
v1v2v3v4 (latest)

Optimal Algorithms for Stochastic Multi-Level Compositional Optimization

15 February 2022
Wei Jiang
Bokun Wang
Yibo Wang
Lijun Zhang
Tianbao Yang
ArXiv (abs)PDFHTML

Papers citing "Optimal Algorithms for Stochastic Multi-Level Compositional Optimization"

13 / 13 papers shown
Title
Optimal Algorithms for Convex Nested Stochastic Composite Optimization
Optimal Algorithms for Convex Nested Stochastic Composite Optimization
Zhe Zhang
Guanghui Lan
72
30
0
19 Nov 2020
Solving Stochastic Compositional Optimization is Nearly as Easy as
  Solving Stochastic Optimization
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization
Tianyi Chen
Yuejiao Sun
W. Yin
126
82
0
25 Aug 2020
Stochastic Multi-level Composition Optimization Algorithms with
  Level-Independent Convergence Rates
Stochastic Multi-level Composition Optimization Algorithms with Level-Independent Convergence Rates
Krishnakumar Balasubramanian
Saeed Ghadimi
A. Nguyen
81
34
0
24 Aug 2020
Tilted Empirical Risk Minimization
Tilted Empirical Risk Minimization
Tian Li
Ahmad Beirami
Maziar Sanjabi
Virginia Smith
79
135
0
02 Jul 2020
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Kaiyi Ji
Junjie Yang
Yingbin Liang
91
50
0
18 Feb 2020
Gradient descent algorithms for Bures-Wasserstein barycenters
Gradient descent algorithms for Bures-Wasserstein barycenters
Sinho Chewi
Tyler Maunu
Philippe Rigollet
Austin J. Stromme
53
87
0
06 Jan 2020
Momentum-Based Variance Reduction in Non-Convex SGD
Momentum-Based Variance Reduction in Non-Convex SGD
Ashok Cutkosky
Francesco Orabona
ODL
96
410
0
24 May 2019
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Liangchen Luo
Yuanhao Xiong
Yan Liu
Xu Sun
ODL
91
602
0
26 Feb 2019
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path
  Integrated Differential Estimator
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
Cong Fang
C. J. Li
Zhouchen Lin
Tong Zhang
104
580
0
04 Jul 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
833
11,961
0
09 Mar 2017
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark Schmidt
280
1,222
0
16 Aug 2016
Accelerating Stochastic Composition Optimization
Accelerating Stochastic Composition Optimization
Mengdi Wang
Ji Liu
Ethan X. Fang
80
148
0
25 Jul 2016
Information-theoretic lower bounds on the oracle complexity of
  stochastic convex optimization
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
Alekh Agarwal
Peter L. Bartlett
Pradeep Ravikumar
Martin J. Wainwright
212
251
0
03 Sep 2010
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