Fetching the paper…
Reading the bibliography…
Stochastic gradient methods (SGMs) have been widely used for solving stochastic optimization problems.
Zur theorie der gesellschaftsspiele
J v Neumann · 1928
Earlier work this paper cites.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
Optimization of conditional value-at-risk
R Tyrrell Rockafellar, Stanislav Uryasev, et al · 2000
Earlier work this paper cites.
Result analysis of the nips 2003 feature selection challenge
Isabelle Guyon, Steve Gunn, Asa Ben-Hur, and Gideon Dror · 2004
Earlier work this paper cites.
Uncertain convex programs: randomized solutions and confidence levels
Giuseppe Calafiore and Marco C Campi · 2005
Earlier work this paper cites.
A neyman-pearson approach to statistical learning
Clayton Scott and Robert Nowak · 2005
Earlier work this paper cites.
The scenario approach to robust control design
Giuseppe C Calafiore and Marco C Campi · 2006
Earlier work this paper cites.
Graph implementations for nonsmooth convex programs
Michael C Grant and Stephen P Boyd · 2008
Earlier work this paper cites.
A sample approximation approach for optimization with probabilistic constraints
James Luedtke and Shabbir Ahmed · 2008
Earlier work this paper cites.
Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
Earlier work this paper cites.
Sample average approximation method for chance constrained programming: theory and applications
Bernardo K Pagnoncelli, Shabbir Ahmed, and Alexander Shapiro · 2009
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Solving variational inequalities with stochastic mirror-prox algorithm
Anatoli Juditsky, Arkadi Nemirovski, and Claire Tauvel · 2011
Earlier work this paper cites.
Neyman-pearson classification, convexity and stochastic constraints
Philippe Rigollet and Xin Tong · 2011
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
Earlier work this paper cites.
Adadelta: an adaptive learning rate method
Matthew D Zeiler · 2012
Earlier work this paper cites.
An augmented lagrangian method for conic convex programming
Necdet Serhat Aybat and Garud Iyengar · 2013
Cited alongside, same era.
Optimal primal-dual methods for a class of saddle point problems
Yunmei Chen, Guanghui Lan, and Yuyuan Ouyang · 2014
Cited alongside, same era.
CVX: Matlab software for disciplined convex programming, version 2.1
Michael Grant and Stephen Boyd · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Lectures on stochastic programming: modeling and theory
Alexander Shapiro, Darinka Dentcheva, and Andrzej Ruszczyński · 2014
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
Later among the works it cites.
Iteration complexity of randomized primal-dual methods for convex-concave saddle point problems
E Yazdandoost Hamedani, A Jalilzadeh, NS Aybat, and UV Shanbhag · 2018
Later among the works it cites.
A primal-dual algorithm for general convex-concave saddle point problems
Erfan Yazdandoost Hamedani and Necdet Serhat Aybat · 2018
Later among the works it cites.
Level-set methods for finite-sum constrained convex optimization
Qihang Lin, Runchao Ma, and Tianbao Yang · 2018
Later among the works it cites.
A level-set method for convex optimization with a feasible solution path
Qihang Lin, Selvaprabu Nadarajah, and Negar Soheili · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mengdi Wang, Yichen Chen, Jialin Liu, and Yuantao Gu · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Incorporating nesterov momentum into adam.(2016)
Timothy Dozat · 2016
Cited alongside, same era.
Monotonic calibrated interpolated look-up tables
Maya Gupta, Andrew Cotter, Jan Pfeifer, Konstantin Voevodski, Kevin Canini, Alexander Mangylov, Wojciech Moczydlowski, and Alexander Van Esbroeck · 2016
Cited alongside, same era.
Stochastic first-order methods with random constraint projection
Mengdi Wang and Dimitri P Bertsekas · 2016
Cited alongside, same era.
A primal-dual type algorithm with the O ( 1 / t ) {O}(1/t) convergence rate for large scale constrained convex programs
Hao Yu and Michael J Neely · 2016
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Iteration-complexity of first-order augmented lagrangian methods for convex conic programming
Zhaosong Lu and Zirui Zhou · 2018
Later among the works it cites.
On the convergence of adam and beyond
J Reddi Sashank, Kale Satyen, and Kumar Sanjiv · 2018
Later among the works it cites.
Adaptive gradient methods with dynamic bound of learning rate
Liangchen Luo, Yuanhao Xiong, Yan Liu, and Xu Sun · 2019
Later among the works it cites.
Fairness constraints: A flexible approach for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P Gummadi · 2019
Later among the works it cites.
Optimal stochastic algorithms for convex-concave saddle-point problems
Renbo Zhao · 2019
Later among the works it cites.
Algorithms for stochastic optimization with function or expectation constraints
Guanghui Lan and Zhiqiang Zhou · 2020
Closest in time.
Primal-dual stochastic gradient method for convex programs with many functional constraints
Yangyang Xu · 2020
Closest in time.
First-order methods for constrained convex programming based on linearized augmented lagrangian function
Yangyang Xu · 2021
Closest in time.
Iteration complexity of inexact augmented lagrangian methods for constrained convex programming
Yangyang Xu · 2021
Closest in time.
Katyusha acceleration for convex finite-sum compositional optimization
Yibo Xu and Yangyang Xu · 2021
Closest in time.