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This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives.
Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition
Thomas M Cover · 1965
Earlier work this paper cites.
On the existence of maximum likelihood estimates in logistic regression models
Adelin Albert and John A Anderson · 1984
Earlier work this paper cites.
Some inequalities for gaussian processes and applications
Yehoram Gordon · 1985
Earlier work this paper cites.
A note on a. albert and ja anderson’s conditions for the existence of maximum likelihood estimates in logistic regression models
Thomas J Santner and Diane E Duffy · 1986
Earlier work this paper cites.
The space of interactions in neural network models
Elizabeth Gardner · 1988
Earlier work this paper cites.
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Earlier work this paper cites.
Partial separation in logistic discrimination
Emmanuel Lesaffre and Adelin Albert · 1989
Earlier work this paper cites.
The strength of weak learnability
Robert E Schapire · 1990
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Bagging, boosting, and c4. 5. in ‘aaai’96 proceedings of the thirteenth national conference on artificial intelligence–volume 1’, 4–8 august 1996, portland, or, usa, 1996
JR Quinlan · 1996
Earlier work this paper cites.
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Leo Breiman · 1999
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Boosting algorithms as gradient descent
Llew Mason, Jonathan Baxter, Peter L Bartlett, and Marcus R Frean · 2000
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Wenxin Jiang · 2001
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Geometric bounds for generalization in boosting
Shie Mannor and Ron Meir · 2001
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Soft margins for adaboost
Gunnar Rätsch, Takashi Onoda, and K-R Müller · 2001
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Empirical margin distributions and bounding the generalization error of combined classifiers
Vladimir Koltchinskii and Dmitry Panchenko · 2002
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On the existence of linear weak learners and applications to boosting
Shie Mannor and Ron Meir · 2002
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Shie Mannor, Ron Meir, and Tong Zhang · 2002
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On the rate of convergence of regularized boosting classifiers
Gilles Blanchard, Gábor Lugosi, and Nicolas Vayatis · 2003
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Peter Bühlmann and Bin Yu · 2003
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Mariya Shcherbina and Brunello Tirozzi · 2003
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Population theory for boosting ensembles
Leo Breiman · 2004
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Process consistency for adaboost
Wenxin Jiang · 2004
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On the Bayes-risk consistency of regularized boosting methods
Gábor Lugosi and Nicolas Vayatis · 2004
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Boosting as a regularized path to a maximum margin classifier
Saharon Rosset, Ji Zhu, and Trevor Hastie · 2004
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Statistical behavior and consistency of classification methods based on convex risk minimization
Tong Zhang · 2004
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Exponential convergence rates in classification
Vladimir Koltchinskii and Olexandra Beznosova · 2005
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Complexities of convex combinations and bounding the generalization error in classification
Vladimir Koltchinskii and Dmitry Panchenko · 2005
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Efficient margin maximizing with boosting
Gunnar Rätsch and Manfred K Warmuth · 2005
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Peter J Bickel, Ya’acov Ritov, and Alon Zakai · 2006
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Peter Buhlmann · 2006
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Precise error analysis of regularized m m -estimators in high dimensions
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Benign overfitting in linear regression
Peter L Bartlett, Philip M Long, Gábor Lugosi, and Alexander Tsigler · 2019
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Two models of double descent for weak features
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A Model of Double Descent for High-dimensional Binary Linear Classification
Zeyu Deng, Abla Kammoun, and Christos Thrampoulidis · 2019
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Surprises in high-dimensional ridgeless least squares interpolation
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Sparse boosting
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How boosting the margin can also boost classifier complexity
Lev Reyzin and Robert E Schapire · 2006
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Adaboost is consistent
Peter L Bartlett and Mikhail Traskin · 2007
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Boosting algorithms: Regularization, prediction and model fitting
Peter Bühlmann and Torsten Hothorn · 2007
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Analysis of boosting algorithms using the smooth margin function
Cynthia Rudin, Robert E Schapire, and Ingrid Daubechies · 2007
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Optimal transport: old and new , volume 338
Cédric Villani · 2008
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Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
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Asymptotics and optimal designs of slope for sparse linear regression
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Simplicity creates inequity: implications for fairness, stereotypes, and interpretability
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The generalization error of random features regression: Precise asymptotics and the double descent curve
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The generalization error of max-margin linear classifiers: High-dimensional asymptotics in the overparametrized regime
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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The impact of regularization on high-dimensional logistic regression
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