Fetching the paper…
Reading the bibliography…
We present a series of new and more favorable margin-based learning guarantees that depend on the empirical margin loss of a predictor.
A course on empirical processes
R. M. Dudley · 1984
Earlier work this paper cites.
Convergence of Stochastic Processess
David Pollard · 1984
Earlier work this paper cites.
Universal Donsker classes and metric entropy
R. M. Dudley · 1987
Earlier work this paper cites.
Asymptotics via empirical processes
David Pollard · 1989
Earlier work this paper cites.
Decision theoretic generalizations of the PAC model for neural net and other learning applications
David Haussler · 1992
Earlier work this paper cites.
A result of Vapnik with applications
M. Anthony and J. Shawe-Taylor · 1993
Earlier work this paper cites.
Support-Vector Networks
Corinna Cortes and Vladimir Vapnik · 1995
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E. Schapire · 1997
Earlier work this paper cites.
Boosting the margin: A new explanation for the effectiveness of voting methods
Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee · 1997
Earlier work this paper cites.
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
Peter L Bartlett · 1998
Earlier work this paper cites.
Generalization performance of support vector machines and other pattern classifiers
Peter L. Bartlett and John Shawe-Taylor · 1998
Cited alongside, same era.
Structural risk minimization over data-dependent hierarchies
John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, and Martin Anthony · 1998
Cited alongside, same era.
Neural Network Learning: Theoretical Foundations
Martin Anthony and Peter L. Bartlett · 1999
Cited alongside, same era.
Empirical margin distributions and bounding the generalization error of combined classifiers
Vladmir Koltchinskii and Dmitry Panchenko · 2002
Cited alongside, same era.
Simplified pac-bayesian margin bounds
David McAllester · 2003
Cited alongside, same era.
Generalization Error Bounds for Bayesian Mixture Algorithms
Ron Meir and Tong Zhang · 2003
Cited alongside, same era.
Smoothness, low noise and fast rates
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
Later among the works it cites.
Tight lower bound on the probability of a binomial exceeding its expectation
Spencer Greenberg and Mehryar Mohri · 2013
Later among the works it cites.
Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
Later among the works it cites.
Probability in High Dimension, APC 550 Lecture Notes
Ramon van Handel · 2016
Later among the works it cites.
Spectrally-normalized margin bounds for neural networks
Peter L. Bartlett, Dylan J. Foster, and Matus Telgarsky · 2017
Later among the works it cites.
Relative deviation learning bounds and generalization with unbounded loss functions
Corinna Cortes, Spencer Greenberg, and Mehryar Mohri · 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…
Max-margin Markov networks
Benjamin Taskar, Carlos Guestrin, and Daphne Koller · 2003
Cited alongside, same era.
Local rademacher complexities
Peter L Bartlett, Olivier Bousquet, Shahar Mendelson, et al · 2005
Cited alongside, same era.
A general agnostic active learning algorithm
Sanjoy Dasgupta, Daniel J. Hsu, and Claire Monteleoni · 2008
Cited alongside, same era.
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Sham M. Kakade, Karthik Sridharan, and Ambuj Tewari · 2008
Cited alongside, same era.
Statistical Learning Theory
Vladimir N. Vapnik
Cited in the paper.
Statistical Learning Theory
Vladimir N. Vapnik
Cited in the paper.
Hypothesis set stability and generalization
Dylan J Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, and Karthik Sridharan · 2019
Later among the works it cites.
Covering number bounds of certain regularized linear function classes
Tong Zhang · 2019
Later among the works it cites.
Near-tight margin-based generalization bounds for support vector machines
Allan Grønlund, Lior Kamma, and Kasper Green Larsen · 2020
Closest in time.
Generalization bounds for deep convolutional neural networks
Philip M. Long and Hanie Sedghi · 2020
Closest in time.