Fetching the paper…
Reading the bibliography…
Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set.
Convex Analysis
R.T. Rockafellar · 1970
Earlier work this paper cites.
Aggregating strategies
Volodimir G. Vovk · 1990
Earlier work this paper cites.
Weak sharp minima in mathematical programming
James V. Burke and Michael C. Ferris · 1993
Earlier work this paper cites.
Error bounds in mathematical programming
Jong-Shi Pang · 1997
Earlier work this paper cites.
The importance of convexity in learning with squared loss
Wee Sun Lee, P. L. Bartlett, and R. C. Williamson · 1998
Earlier work this paper cites.
Statistical Learning Theory
Vladimir N. Vapnik · 1998
Earlier work this paper cites.
Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
Earlier work this paper cites.
Introductory lectures on convex optimization: a basic course
Yurii Nesterov · 2004
Earlier work this paper cites.
Local rademacher complexities
Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson · 2005
Earlier work this paper cites.
Empirical minimization
Peter L. Bartlett and Shahar Mendelson · 2006
Earlier work this paper cites.
Local rademacher complexities and oracle inequalities in risk minimization
Vladimir Koltchinskii · 2006
Earlier work this paper cites.
Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, and Satyen Kale · 2007
Earlier work this paper cites.
Pegasos: Primal estimated sub-gradient solver for svm
Shai Shalev-Shwartz, Yoram Singer, and Nathan Srebro · 2007
Earlier work this paper cites.
Learning theory estimates via integral operators and their approximations
Steve Smale and Ding-Xuan Zhou · 2007
Earlier work this paper cites.
On the generalization ability of online strongly convex programming algorithms
Sham M. Kakade and Ambuj Tewari · 2008
Earlier work this paper cites.
Fast rates for regularized objectives
Karthik Sridharan, Shai Shalev-Shwartz, and Nathan Srebro · 2008
Earlier work this paper cites.
Efficient online and batch learning using forward backward splitting
John Duchi and Yoram Singer · 2009
Earlier work this paper cites.
Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Lan, and Alexander Shapiro · 2009
Earlier work this paper cites.
Stochastic convex optimization
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2009
Cited alongside, same era.
Error bounds for convex polynomials
W. H. Yang · 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.
Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization
Elad Hazan and Satyen Kale · 2011
Cited alongside, same era.
Stochastic methods for l 1 {}_{\mbox{1}} -regularized loss minimization
Shai Shalev-Shwartz and Ambuj Tewari · 2011
Cited alongside, same era.
Making gradient descent optimal for strongly convex stochastic optimization
Alexander Rakhlin, Ohad Shamir, and Karthik Sridharan · 2012
Cited alongside, same era.
Linear convergence of first order methods for non-strongly convex optimization
I. Necoara, Yu. Nesterov, and F. Glineur · 2015
Later among the works it cites.
Fast rates in statistical and online learning
Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, and Robert C. Williamson · 2015
Later among the works it cites.
Error bounds, quadratic growth, and linear convergence of proximal methods
Dmitriy Drusvyatskiy and Adrian S. Lewis · 2016
Later among the works it cites.
Generalization of erm in stochastic convex optimization: The dimension strikes back
Vitaly Feldman · 2016
Later among the works it cites.
Faster eigenvector computation via shift-and-invert preconditioning
Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, and Aaron Sidford · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Non-strongly-convex smooth stochastic approximation with convergence rate o(1/n)
Francis R. Bach and Eric Moulines · 2013
Cited alongside, same era.
Global error bounds for piecewise convex polynomials
Guoyin Li · 2013
Cited alongside, same era.
Optimal rates for stochastic convex optimization under tsybakov noise condition
Aaditya Ramdas and Aarti Singh · 2013
Cited alongside, same era.
Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
Cited alongside, same era.
Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization
Anatoli Juditsky and Yuri Nesterov · 2014
Cited alongside, same era.
Excess risk bounds for exponentially concave losses
Mehrdad Mahdavi and Rong Jin · 2014
Cited alongside, same era.
Alon Gonen and Shai Shalev-Shwartz · 2016
Later among the works it cites.
Fast rates with unbounded losses
Peter D. Grünwald and Nishant A. Mehta · 2016
Later among the works it cites.
Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
Hamed Karimi, Julie Nutini, and Mark W. Schmidt · 2016
Later among the works it cites.
Combining adversarial guarantees and stochastic fast rates in online learning
Wouter M. Koolen, Peter Grünwald, and Tim van Erven · 2016
Later among the works it cites.
Guoyin Li and Ting Kei Pong · 2016
Later among the works it cites.
Metagrad: Multiple learning rates in online learning
Tim van Erven and Wouter M. Koolen · 2016
Later among the works it cites.
Accelerate stochastic subgradient method by leveraging local error bound
Yi Xu, Qihang Lin, and Tianbao Yang · 2016
Later among the works it cites.
Rsg: Beating subgradient method without smoothness and strong convexity
Tianbao Yang and Qihang Lin · 2016
Later among the works it cites.
New analysis of linear convergence of gradient-type methods via unifying error bound conditions
Hui Zhang · 2016
Later among the works it cites.
Fast rates with high probability in exp-concave statistical learning
Nishant A. Mehta · 2017
Later among the works it cites.
Stochastic convex optimization: Faster local growth implies faster global convergence
Yi Xu, Qihang Lin, and Tianbao Yang · 2017
Later among the works it cites.
Lijun Zhang, Tianbao Yang, and Rong Jin · 2017
Later among the works it cites.