Fetching the paper…
Reading the bibliography…
Stochastic optimization problems often involve data distributions that change in reaction to the decision variables.
On a space of completely additive functions
Leonid Vitalyevich Kantorovich and Gennady S Rubinstein · 1958
Earlier work this paper cites.
A method for solving the convex programming problem with convergence rate O ( 1 / k 2 ) O(1/k^{2})
Yu. Nesterov · 1983
Earlier work this paper cites.
Stochastic dynamic optimization approaches and computation
Pravin Varaiya and RJ-B Wets · 1988
Earlier work this paper cites.
Learning time-varying concepts
Anthony Kuh, Thomas Petsche, and Ronald L Rivest · 1991
Earlier work this paper cites.
Learning with a slowly changing distribution
Peter L Bartlett · 1992
Earlier work this paper cites.
Discrete event systems: Sensitivity analysis and stochastic optimization by the score function method
Reuven Y Rubinstein and Alexander Shapiro · 1993
Earlier work this paper cites.
Nonlinear programming
Dimitri P Bertsekas · 1997
Earlier work this paper cites.
A class of stochastic programs withdecision dependent random elements
Tore W Jonsbrten, Roger JB Wets, and David L Woodruff · 1998
Earlier work this paper cites.
Variational Analysis
R.T. Rockafellar and R.J-B. Wets · 1998
Earlier work this paper cites.
Strategic planning under uncertainty: Stochastic integer programming approaches
Shabbir Ahmed · 2000
Earlier work this paper cites.
Learning changing concepts by exploiting the structure of change
Peter L Bartlett, Shai Ben-David, and Sanjeev R Kulkarni · 2000
Earlier work this paper cites.
Juan C Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt · 2002
Earlier work this paper cites.
Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
Earlier work this paper cites.
Adversarial classification
Nilesh Dalvi, Pedro Domingos, Sumit Sanghai, and Deepak Verma · 2004
Earlier work this paper cites.
Introductory lectures on convex optimization
Y. Nesterov · 2004
Earlier work this paper cites.
Optimization under exogenous and endogenous uncertainty
Jitka Dupacová · 2006
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
Earlier work this paper cites.
Efficient online and batch learnng using forward backward splitting
John Duchi and Yoram Singer · 2009
Cited alongside, same era.
Primal-dual subgradient methods for convex problems
Yurii Nesterov · 2009
Cited alongside, same era.
Dual averaging methods for regularized stochastic learning and online optimization
Lin Xiao · 2010
Cited alongside, same era.
Static prediction games for adversarial learning problems
Michael Brückner, Christian Kanzow, and Tobias Scheffer · 2012
Cited alongside, same era.
Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework
Saeed Ghadimi and Guanghui Lan · 2012
Cited alongside, same era.
Give me some credit
Kaggle · 2012
Cited alongside, same era.
First-order methods in optimization
Amir Beck · 2017
Later among the works it cites.
Universal intermediate gradient method for convex problems with inexact oracle
Pavel Dvurechensky, Alexander Gasnikov, and Dmitry Kamzolov · 2017
Later among the works it cites.
A survey of algorithms and analysis for adaptive online learning
Brendan McMahan · 2017
Later among the works it cites.
Decision-dependent probabilities in stochastic programs with recourse
Lars Hellemo, Paul I Barton, and Asgeir Tomasgard · 2018
Later among the works it cites.
Lectures on convex optimization
Yurii Nesterov · 2018
Later among the works it cites.
Stochastic (approximate) proximal point methods: Convergence, optimality, and adaptivity
Hilal Asi and John C Duchi · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An optimal method for stochastic composite optimization
Guanghui Lan · 2012
Cited alongside, same era.
Online learning and online convex optimization
Shai Shalev-Shwartz · 2012
Cited alongside, same era.
Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization, ii: shrinking procedures and optimal algorithms
Saeed Ghadimi and Guanghui Lan · 2013
Cited alongside, same era.
Convex optimization: Algorithms and complexity
Sébastien Bubeck · 2014
Cited alongside, same era.
First-order methods of smooth convex optimization with inexact oracle
Olivier Devolder, François Glineur, and Yurii Nesterov · 2014
Cited alongside, same era.
A survey on concept drift adaptation
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia · 2014
Cited alongside, same era.
Later among the works it cites.
A universally optimal multistage accelerated stochastic gradient method
Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban, and Asuman Ozdaglar · 2019
Later among the works it cites.
Optimal exploration–exploitation in a multi-armed bandit problem with non-stationary rewards
Omar Besbes, Yonatan Gur, and Assaf Zeevi · 2019
Later among the works it cites.
Stochastic model-based minimization of weakly convex functions
Damek Davis and Dmitriy Drusvyatskiy · 2019
Later among the works it cites.
Andrei Kulunchakov and Julien Mairal · 2019
Later among the works it cites.
A generic acceleration framework for stochastic composite optimization
Andrei Kulunchakov and Julien Mairal · 2019
Later among the works it cites.
The social cost of strategic classification
Smitha Milli, John Miller, Anca D. Dragan, and Moritz Hardt · 2019
Later among the works it cites.
Inexact model: A framework for optimization and variational inequalities
Fedor Stonyakin, Alexander Gasnikov, Alexander Tyurin, Dmitry Pasechnyuk, Artem Agafonov, Pavel Dvurechensky, Darina Dvinskikh, Alexey Kroshnin, and Victorya Piskunova · 2019
Later among the works it cites.
Analysis of sgd with biased gradient estimators
Ahmad Ajalloeian and Sebastian U Stich · 2020
Closest in time.
Causal feature discovery through strategic modification
Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, and Juba Ziani · 2020
Closest in time.
Stochastic optimization for performative prediction
Celestine Mendler-Dünner, Juan C Perdomo, Tijana Zrnic, and Moritz Hardt · 2020
Closest in time.