Fetching the paper…
Reading the bibliography…
We investigate the convergence of stochastic mirror descent (SMD) under interpolation in relatively smooth and smooth convex optimization.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
Une propriété topologique des sous-ensembles analytiques réels, 1963
Stanislaw Łojasiewicz · 1963
Earlier work this paper cites.
Gradient methods for solving equations and inequalities
Boris T Polyak · 1964
Earlier work this paper cites.
Problem Complexity and Method Efficiency in Optimization
A.S. Nemirovsky and D.B. Yudin · 1983
Earlier work this paper cites.
Introduction to optimization
Boris Polyak · 1987
Earlier work this paper cites.
From on-line to batch learning
Nick Littlestone · 1989
Earlier work this paper cites.
The weighted majority algorithm
Nick Littlestone and Manfred K Warmuth · 1994
Earlier work this paper cites.
Legendre functions and the method of random bregman projections
Heinz H Bauschke, Jonathan M Borwein, et al · 1997
Earlier work this paper cites.
Exponentiated gradient versus gradient descent for linear predictors
Jyrki Kivinen and Manfred K. Warmuth · 1997
Earlier work this paper cites.
The ordered subsets mirror descent optimization method with applications to tomography
Aharon Ben-Tal, Tamar Margalit, and Arkadi Nemirovski · 2001
Earlier work this paper cites.
General convergence results for linear discriminant updates
Adam J Grove, Nick Littlestone, and Dale Schuurmans · 2001
Earlier work this paper cites.
Adaptive and self-confident on-line learning algorithms
Peter Auer, Nicolò Cesa-Bianchi, and Claudio Gentile · 2002
Earlier work this paper cites.
Bregman monotone optimization algorithms
Heinz H Bauschke, Jonathan M Borwein, and Patrick L Combettes · 2003
Earlier work this paper cites.
Mirror descent and nonlinear projected subgradient methods for convex optimization
Amir Beck and Marc Teboulle · 2003
Earlier work this paper cites.
Parallel and distributed computation: numerical methods
Dimitri P Bertsekas and John N Tsitsiklis · 2003
Earlier work this paper cites.
Subgradient methods
Stephen Boyd, Lin Xiao, and Almir Mutapcic · 2003
Earlier work this paper cites.
The robustness of the p-norm algorithms
Claudio Gentile · 2003
Earlier work this paper cites.
On the generalization ability of on-line learning algorithms
Nicolo Cesa-Bianchi, Alex Conconi, and Claudio Gentile · 2004
Earlier work this paper cites.
Fundamentals of convex analysis
Jean-Baptiste Hiriart-Urruty and Claude Lemaréchal · 2004
Earlier work this paper cites.
Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
Earlier work this paper cites.
Bounds for regret-matching algorithms
Amy Greenwald, Zheng Li, and Casey Marks · 2006
Earlier work this paper cites.
From external to internal regret
Avrim Blum and Yishay Mansour · 2007
Earlier work this paper cites.
Exponentiated gradient algorithms for conditional random fields and max-margin markov networks
Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras Pérez, and Peter Bartlett · 2008
Earlier work this paper cites.
Image deblurring with poisson data: from cells to galaxies
Mario Bertero, Patrizia Boccacci, Gabriele Desiderà, and Giuseppe Vicidomini · 2009
Earlier work this paper cites.
Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
Cited alongside, same era.
Composite objective mirror descent
John C Duchi, Shai Shalev-Shwartz, Yoram Singer, and Ambuj Tewari · 2010
Cited alongside, same era.
Smoothing techniques for computing nash equilibria of sequential games
Samid Hoda, Andrew Gilpin, Javier Pena, and Tuomas Sandholm · 2010
Cited alongside, same era.
Adaptive bound optimization for online convex optimization
H Brendan McMahan and Matthew Streeter · 2010
Cited alongside, same era.
Optimistic rates for learning with a smooth loss
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
Cited alongside, same era.
Relatively smooth convex optimization by first-order methods, and applications
Haihao Lu, Robert M Freund, and Yurii Nesterov · 2018
Later among the works it cites.
The power of interpolation: Understanding the effectiveness of SGD in modern over-parametrized learning
Siyuan Ma, Raef Bassily, and Mikhail Belkin · 2018
Later among the works it cites.
Lectures on convex optimization , volume 137
Yurii Nesterov · 2018
Later among the works it cites.
L4: Practical loss-based stepsize adaptation for deep learning
Michal Rolinek and Georg Martius · 2018
Later among the works it cites.
Stochastic gradient/mirror descent: Minimax optimality and implicit regularization
Navid Azizan and Babak Hassibi · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Matthew Streeter and H Brendan McMahan · 2010
Cited alongside, same era.
Distributed algorithms via gradient descent for fisher markets
Benjamin Birnbaum, Nikhil R Devanur, and Lin Xiao · 2011
Cited alongside, same era.
LIBSVM: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
Cited alongside, same era.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Cited alongside, same era.
Optimal distributed online prediction using mini-batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao · 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.
Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization
Elad Hazan and Satyen Kale · 2014
Cited alongside, same era.
Navid Azizan, Sahin Lale, and Babak Hassibi · 2019
Later among the works it cites.
Sgd: General analysis and improved rates
Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, and Peter Richtárik · 2019
Later among the works it cites.
Revisiting the polyak step size
Elad Hazan and Sham Kakade · 2019
Later among the works it cites.
On the convergence of stochastic gradient descent with adaptive stepsizes
Xiaoyu Li and Francesco Orabona · 2019
Later among the works it cites.
A modern introduction to online learning
Francesco Orabona · 2019
Later among the works it cites.
Adaptive mirror descent algorithms for convex and strongly convex optimization problems with functional constraints
Fedor Sergeevich Stonyakin, M Alkousa, Aleksei Nikolaevich Stepanov, and Aleksandr Aleksandrovich Titov · 2019
Later among the works it cites.
Training neural networks for and by interpolation
Leonard Berrada, Andrew Zisserman, and M Pawan Kumar · 2020
Later among the works it cites.
Randomized bregman coordinate descent methods for non-lipschitz optimization
Tianxiang Gao, Songtao Lu, Jia Liu, and Chris Chu · 2020
Later among the works it cites.
Dual-free stochastic decentralized optimization with variance reduction
Hadrien Hendrikx, Francis Bach, and Laurent Massoulié · 2020
Later among the works it cites.
Faster algorithms for extensive-form game solving via improved smoothing functions
Christian Kroer, Kevin Waugh, Fatma Kılınç-Karzan, and Tuomas Sandholm · 2020
Later among the works it cites.
Fast stochastic bregman gradient methods: Sharp analysis and variance reduction
Radu Alexandru Dragomir, Mathieu Even, and Hadrien Hendrikx · 2021
Closest in time.
Sgd for structured nonconvex functions: Learning rates, minibatching and interpolation
Robert Gower, Othmane Sebbouh, and Nicolas Loizou · 2021
Closest in time.
Fastest rates for stochastic mirror descent methods
Filip Hanzely and Peter Richtarik · 2021
Closest in time.
Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence
Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, and Simon Lacoste-Julien · 2021
Closest in time.
Stochastic gradient descent with polyak’s learning rate
Mariana Prazeres and Adam M Oberman · 2021
Closest in time.
Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
Closest in time.
Convergence of sequences: A survey
Barbara Franci and Sergio Grammatico · 2022
Closest in time.
Improved complexities for stochastic conditional gradient methods under interpolation-like conditions
Tesi Xiao, Krishnakumar Balasubramanian, and Saeed Ghadimi · 2022
Closest in time.