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The local Rademacher complexity framework is one of the most successful general-purpose toolboxes for establishing sharp excess risk bounds for statistical estimators based on the framework of empirical risk minimization.
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Convex Analysis
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On the uniform convergence of relative frequencies of events to their probabilities
VN Vapnik and A Ya Chervonenkis · 1971
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Predicting
David Haussler, Nick Littlestone, and Manfred K Warmuth · 1994
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Sharper bounds for gaussian and empirical processes
Michel Talagrand · 1994
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New concentration inequalities in product spaces
Michel Talagrand · 1996
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The mixture approach to universal model selection
Olivier Catoni · 1997
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On talagrand’s deviation inequalities for product measures
Michel Ledoux · 1997
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A sharp concentration inequality with applications
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2000
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Rademacher processes and bounding the risk of function learning
Vladimir Koltchinskii and Dmitriy Panchenko · 2000
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Topics in non-parametric statistics
Arkadi Nemirovski · 2000
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Empirical Processes in M-estimation , volume 6
Sara van de Geer · 2000
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Combining different procedures for adaptive regression
Yuhong Yang · 2000
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Rademacher penalties and structural risk minimization
Vladimir Koltchinskii · 2001
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Competitive on-line statistics
Volodya Vovk · 2001
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Model selection and error estimation
Peter L Bartlett, Stéphane Boucheron, and Gábor Lugosi · 2002
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A bennett concentration inequality and its application to suprema of empirical processes
Olivier Bousquet · 2002
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Amir Beck and Marc Teboulle · 2003
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Boosting with the l2 loss: regression and classification
Peter Bühlmann and Bin Yu · 2003
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Optimal rates of aggregation
Alexandre B Tsybakov · 2003
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On the generalization ability of on-line learning algorithms
Nicolo Cesa-Bianchi, Alex Conconi, and Claudio Gentile · 2004
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Complexity regularization via localized random penalties
Gábor Lugosi and Marten Wegkamp · 2004
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Local rademacher complexities
Peter L Bartlett, Olivier Bousquet, and Shahar Mendelson · 2005
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Theory of classification: A survey of some recent advances
Stéphane Boucheron, Olivier Bousquet, and Gábor Lugosi · 2005
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Concentration around the mean for maxima of empirical processes
Thierry Klein and Emmanuel Rio · 2005
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Empirical minimization
Peter L Bartlett and Shahar Mendelson · 2006
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Online non-parametric regression
Alexander Rakhlin and Karthik Sridharan · 2014
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Early stopping and non-parametric regression: an optimal data-dependent stopping rule
Garvesh Raskutti, Martin J Wainwright, and Bin Yu · 2014
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Learning with square loss: Localization through offset rademacher complexity
Tengyuan Liang, Alexander Rakhlin, and Karthik Sridharan · 2015
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Learning without concentration
Shahar Mendelson · 2015
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Nonparametric stochastic approximation with large step-sizes
Aymeric Dieuleveut and Francis Bach · 2016
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Iterative regularization for learning with convex loss functions
Junhong Lin, Lorenzo Rosasco, and Ding-Xuan Zhou · 2016
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Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
Cited alongside, same era.
Local rademacher complexities and oracle inequalities in risk minimization
Vladimir Koltchinskii · 2006
Cited alongside, same era.
Concentration inequalities for functions of independent variables
Andreas Maurer · 2006
Cited alongside, same era.
Discussion: Local Rademacher complexities and oracle inequalities in risk minimization
A. B. Tsybakov · 2006
Cited alongside, same era.
On early stopping in gradient descent learning
Yuan Yao, Lorenzo Rosasco, and Andrea Caponnetto · 2007
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Progressive mixture rules are deviation suboptimal
Jean-Yves Audibert · 2008
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The lower tail of random quadratic forms with applications to ordinary least squares
Roberto Oliveira · 2016
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Optimal exponential bounds for aggregation of density estimators
Pierre C Bellec · 2017
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Empirical entropy, minimax regret and minimax risk
Alexander Rakhlin, Karthik Sridharan, and Alexandre B Tsybakov · 2017
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Optimal learning with bernstein online aggregation
Olivier Wintenberger · 2017
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Logistic regression: The importance of being improper
Dylan J. Foster, Satyen Kale, Haipeng Luo, Mehryar Mohri, and Karthik Sridharan · 2018
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Learning without concentration for general loss functions
Shahar Mendelson · 2018
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On optimality of empirical risk minimization in linear aggregation
Adrien Saumard · 2018
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High-dimensional probability: An introduction with applications in data science , volume 47
Roman Vershynin · 2018
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Localization of vc classes: Beyond local rademacher complexities
Nikita Zhivotovskiy and Steve Hanneke · 2018
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Mean estimation and regression under heavy-tailed distributions: A survey
Gábor Lugosi and Shahar Mendelson · 2019
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An unrestricted learning procedure
Shahar Mendelson · 2019
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Jaouad Mourtada and Stéphane Gaïffas · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
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Early stopping for kernel boosting algorithms: A general analysis with localized complexities
Yuting Wei, Fanny Yang, and Martin J Wainwright · 2019
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Suboptimality of constrained least squares and improvements via non-linear predictors
Tomas Vaškevičius and Nikita Zhivotovskiy · 2020
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The statistical complexity of early-stopped mirror descent
Tomas Vaškevičius, Varun Kanade, and Patrick Rebeschini · 2020
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Fast classification rates without standard margin assumptions
Olivier Bousquet and Nikita Zhivotovskiy · 2021
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Stability and deviation optimal risk bounds with convergence rate
Yegor Klochkov and Nikita Zhivotovskiy · 2021
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Exponential savings in agnostic active learning through abstention
Nikita Puchkin and Nikita Zhivotovskiy · 2021
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Localization, convexity, and star aggregation
Suhas Vijaykumar · 2021
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Distribution-free robust linear regression
Jaouad Mourtada, Tomas Vaškevičius, and Nikita Zhivotovskiy · 2022
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