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Contemporary machine learning applications often involve classification tasks with many classes.
A note on the inequalities for tail probability of the multivariate normal distribution
YS Sathe and SR Lingras · 1980
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Cox’s regression model for counting processes: a large sample study
Per Kragh Andersen and Richard D Gill · 1982
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On Milman’s inequality and random subspaces which escape through a mesh in ℝ n \mathbb{R}^{n}
Yehoram Gordon · 1988
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Probability in Banach Spaces: isoperimetry and processes
Michel Ledoux and Michel Talagrand · 1991
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Solving multiclass learning problems via error-correcting output codes
Thomas G Dietterich and Ghulum Bakiri · 1994
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Multi-class support vector machines
Jason Weston and Chris Watkins · 1998
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Multicategory classification by support vector machines
Erin J Bredensteiner and Kristin P Bennett · 1999
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Reducing multiclass to binary: A unifying approach for margin classifiers
Erin L Allwein, Robert E Schapire, and Yoram Singer · 2000
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On the algorithmic implementation of multiclass kernel-based vector machines
Koby Crammer and Yoram Singer · 2001
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Round robin classification
Johannes Fürnkranz · 2002
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Combining discriminant models with new multi-class svms
Yann Guermeur · 2002
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Empirical margin distributions and bounding the generalization error of combined classifiers
Vladimir Koltchinskii, Dmitry Panchenko, et al · 2002
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On multivariate gaussian tails
Enkelejd Hashorva and Jürg Hüsler · 2003
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Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data
Yoonkyung Lee, Yi Lin, and Grace Wahba · 2004
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In defense of one-vs-all classification
Ryan Rifkin and Aldebaro Klautau · 2004
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Statistical analysis of some multi-category large margin classification methods
Tong Zhang · 2004
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Support vector machines for classification in remote sensing
Mahesh Pal and PM Mather · 2005
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Compressed sensing
David L Donoho · 2006
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On the consistency of multiclass classification methods
Ambuj Tewari and Peter L Bartlett · 2007
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Various thresholds for ℓ 1 \ell_{1} -optimization in compressed sensing
Mihailo Stojnic · 2009
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New null space results and recovery thresholds for matrix rank minimization
Samet Oymak and Babak Hassibi · 2010
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The noise-sensitivity phase transition in compressed sensing
David L Donoho, Arian Maleki, and Andrea Montanari · 2011
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The lasso risk for gaussian matrices
Mohsen Bayati and Andrea Montanari · 2012
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The convex geometry of linear inverse problems
Venkat Chandrasekaran, Benjamin Recht, Pablo A Parrilo, and Alan S Willsky · 2012
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Living on the edge: A geometric theory of phase transitions in convex optimization
Dennis Amelunxen, Martin Lotz, Michael B McCoy, and Joel A Tropp · 2013
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Extreme multi class classification
Anna Choromanska, Alekh Agarwal, and John Langford · 2013
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An invitation to compressive sensing
Simon Foucart and Holger Rauhut · 2013
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Noureddine El Karoui · 2013
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The squared-error of generalized lasso: A precise analysis
Samet Oymak, Christos Thrampoulidis, and Babak Hassibi · 2013
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Cost-sensitive multiclass classification risk bounds
Bernardo Avila Pires, Csaba Szepesvari, and Mohammad Ghavamzadeh · 2013
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A framework to characterize performance of lasso algorithms
Mihailo Stojnic · 2013
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Symbol error rate performance of box-relaxation decoders in massive mimo
Christos Thrampoulidis, Weiyu Xu, and Babak Hassibi · 2018
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Universality in learning from linear measurements
Ehsan Abbasi, Fariborz Salehi, and Babak Hassibi · 2019
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Algorithmic analysis and statistical estimation of slope via approximate message passing
Zhiqi Bu, Jason Klusowski, Cynthia Rush, and Weijie Su · 2019
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Fundamental barriers to high-dimensional regression with convex penalties
Michael Celentano and Andrea Montanari · 2019
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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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Rademacher complexity margin bounds for learning with a large number of classes
Vitaly Kuznetsov, Mehryar Mohri, and U Syed · 2015
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Multi-class svms: From tighter data-dependent generalization bounds to novel algorithms
Yunwen Lei, Urun Dogan, Alexander Binder, and Marius Kloft · 2015
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Lasso with non-linear measurements is equivalent to one with linear measurements
Christos Thrampoulidis, Ehsan Abbasi, and Babak Hassibi · 2015
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Regularized linear regression: A precise analysis of the estimation error
Christos Thrampoulidis, Samet Oymak, and Babak Hassibi · 2015
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Deep neural networks for youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
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Structured prediction theory based on factor graph complexity
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, and Scott Yang · 2016
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High dimensional robust m-estimation: Asymptotic variance via approximate message passing
David Donoho and Andrea Montanari · 2016
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Hong Hu and Yue M Lu · 2019
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Data-dependent generalization bounds for multi-class classification
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Asymptotic bayes risk for gaussian mixture in a semi-supervised setting
Marc Lelarge and Leo Miolane · 2019
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A large scale analysis of logistic regression: asymptotic performance and new insights
X. Mai, Z. Liao, and R. Couillet · 2019
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A large scale analysis of logistic regression: Asymptotic performance and new insights
Xiaoyi Mai, Zhenyu Liao, and Romain Couillet · 2019
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Andrea Montanari, Feng Ruan, Youngtak Sohn, and Jun Yan · 2019
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Sampled softmax with random fourier features
Ankit Singh Rawat, Jiecao Chen, Felix Xinnan X Yu, Ananda Theertha Suresh, and Sanjiv Kumar · 2019
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The impact of regularization on high-dimensional logistic regression
Fariborz Salehi, Ehsan Abbasi, and Babak Hassibi · 2019
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A modern maximum-likelihood theory for high-dimensional logistic regression
Pragya Sur and Emmanuel J Candès · 2019
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Does slope outperform bridge regression?
Shuaiwen Wang, Haolei Weng, and Arian Maleki · 2019
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Visualising basins of attraction for the cross-entropy and the squared error neural network loss functions
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Exploring the role of loss functions in multiclass classification
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Precise tradeoffs in adversarial training for linear regression
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On the precise error analysis of support vector machines
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Analytic study of double descent in binary classification: The impact of loss
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Regularization in high-dimensional regression and classification via random matrix theory
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On the global convergence rates of softmax policy gradient methods
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Fundamental limits of ridge-regularized empirical risk minimization in high dimensions
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Sharp asymptotics and optimal performance for inference in binary models
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