Fetching the paper…
Reading the bibliography…
Although there exist plentiful theories of empirical risk minimization (ERM) for supervised learning, current theoretical understandings of ERM for a related problem---stochastic convex optimization (SCO), are limited.
Problem complexity and method efficiency in optimization
A. Nemirovski and D. B. Yudin · 1983
Earlier work this paper cites.
On the method of bounded differences
Colin McDiarmid · 1989
Earlier work this paper cites.
The volume of convex bodies and Banach space geometry
Gilles Pisier · 1989
Earlier work this paper cites.
Equivalence of models for polynomial learnability
David Haussler, Michael Kearns, Nick Littlestone, and Manfred K. Warmuth · 1991
Earlier work this paper cites.
Probability in Banach Spaces: Isoperimetry and Processes
Michel Ledoux and Michel Talagrand · 1991
Earlier work this paper cites.
The importance of convexity in learning with squared loss
Wee Sun Lee, Peter L. Bartlett, and Robert C. Williamson · 1996
Earlier work this paper cites.
Statistical Learning Theory
Vladimir N. Vapnik · 1998
Earlier work this paper cites.
The Nature of Statistical Learning Theory
Vladimir Vapnik · 2000
Earlier work this paper cites.
Rademacher and gaussian complexities: risk bounds and structural results
Peter L. Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Some extensions of an inequality of vapnik and chervonenkis
Dmitriy Panchenko · 2002
Earlier work this paper cites.
Learning with kernels : support vector machines, regularization, optimization, and beyond
Bernhard Schölkopf and Alexander J. Smola · 2002
Earlier work this paper cites.
Stochastic Approximation and Recursive Algorithms and Applications
Harold J. Kushner and G. George Yin · 2003
Earlier work this paper cites.
Generalization error bounds for bayesian mixture algorithms
Ron Meir and Tong Zhang · 2003
Earlier work this paper cites.
Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
Introductory lectures on convex optimization: a basic course , volume 87 of Applied optimization
Yurii Nesterov · 2004
Cited alongside, same era.
Optimal aggregation of classifiers in statistical learning
Alexandre B. Tsybakov · 2004
Cited alongside, same era.
Local rademacher complexities
Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson · 2005
Cited alongside, same era.
Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, and Satyen Kale · 2007
Cited alongside, same era.
Learning theory estimates via integral operators and their approximations
Steve Smale and Ding-Xuan Zhou · 2007
Cited alongside, same era.
Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
Cited alongside, same era.
Making gradient descent optimal for strongly convex stochastic optimization
Alexander Rakhlin, Ohad Shamir, and Karthik Sridharan · 2012
Later among the works it cites.
Non-strongly-convex smooth stochastic approximation with convergence rate O ( 1 / n ) {O}(1/n)
Francis Bach and Eric Moulines · 2013
Later among the works it cites.
Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
Later among the works it cites.
One-bit compressed sensing by linear programming
Yaniv Plan and Roman Vershynin · 2013
Later among the works it cites.
Linear convergence with condition number independent access of full gradients
Lijun Zhang, Mehrdad Mahdavi, and Rong Jin · 2013
Later among the works it cites.
Understanding Machine Learning: From Theory to Algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Stochastic convex optimization
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2009
Cited alongside, same era.
Fast rates for regularized objectives
Karthik Sridharan, Shai Shalev-shwartz, and Nathan Srebro · 2009
Cited alongside, same era.
Optimistic rates for learning with a smooth loss
Nathan Srebro, Karthik Sridharan, 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.
Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems
Vladimir Koltchinskii · 2011
Cited alongside, same era.
Non-asymptotic analysis of stochastic approximation algorithms for machine learning
Eric Moulines and Francis R. Bach · 2011
Cited alongside, same era.
Later among the works it cites.
Lectures on Stochastic Programming: Modeling and Theory
Alexander Shapiro, Darinka Dentcheva, and Andrzej Ruszczyński · 2014
Later among the works it cites.
Learning with deep cascades
Giulia Desalvo, Mehryar Mohri, and Umar Syed · 2015
Later among the works it cites.
Fast rates for exp-concave empirical risk minimization
Tomer Koren and Kfir Levy · 2015
Later among the works it cites.
Lower and upper bounds on the generalization of stochastic exponentially concave optimization
Mehrdad Mahdavi, Lijun Zhang, and Rong Jin · 2015
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.
Alon Gonen and Shai Shalev-Shwartz · 2016
Later among the works it cites.
Fast rates with high probability in exp-concave statistical learning
Nishant A. Mehta · 2016
Later among the works it cites.