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Less than a Single Pass: Stochastically Controlled Stochastic Gradient
  Method

Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method

12 September 2016
Lihua Lei
Michael I. Jordan
ArXivPDFHTML

Papers citing "Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method"

14 / 14 papers shown
Title
Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex
  Finite Sum Problems
Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems
Yanjie Zhong
Jiaqi Li
Soumendra Lahiri
22
1
0
29 Jan 2024
Sampling Through the Lens of Sequential Decision Making
Sampling Through the Lens of Sequential Decision Making
J. Dou
Alvin Pan
Runxue Bao
Haiyi Mao
Lei Luo
Zhi-Hong Mao
26
19
0
17 Aug 2022
SADAM: Stochastic Adam, A Stochastic Operator for First-Order
  Gradient-based Optimizer
SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer
Wei Zhang
Yun-Jian Bao
ODL
25
1
0
20 May 2022
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method
Zhize Li
36
14
0
21 Mar 2021
Optimization for Supervised Machine Learning: Randomized Algorithms for
  Data and Parameters
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters
Filip Hanzely
32
0
0
26 Aug 2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Samuel Horváth
Lihua Lei
Peter Richtárik
Michael I. Jordan
57
30
0
13 Feb 2020
Aggregated Gradient Langevin Dynamics
Aggregated Gradient Langevin Dynamics
Chao Zhang
Jiahao Xie
Zebang Shen
P. Zhao
Tengfei Zhou
Hui Qian
20
1
0
21 Oct 2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite
  Nonconvex Optimization
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
Nhan H. Pham
Lam M. Nguyen
Dzung Phan
Quoc Tran-Dinh
11
139
0
15 Feb 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep
  Learning
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
Aaron Defazio
Léon Bottou
UQCV
DRL
13
112
0
11 Dec 2018
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with
  $β$-Divergences
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with βββ-Divergences
Jeremias Knoblauch
Jack Jewson
Theodoros Damoulas
14
56
0
06 Jun 2018
Stochastic Zeroth-order Optimization via Variance Reduction method
Stochastic Zeroth-order Optimization via Variance Reduction method
L. Liu
Minhao Cheng
Cho-Jui Hsieh
Dacheng Tao
19
19
0
30 May 2018
Double Quantization for Communication-Efficient Distributed Optimization
Double Quantization for Communication-Efficient Distributed Optimization
Yue Yu
Jiaxiang Wu
Longbo Huang
MQ
19
57
0
25 May 2018
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Difan Zou
Pan Xu
Quanquan Gu
BDL
24
31
0
13 Feb 2018
A Proximal Stochastic Gradient Method with Progressive Variance
  Reduction
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
Lin Xiao
Tong Zhang
ODL
84
736
0
19 Mar 2014
1