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Extensive empirical evidence reveals that, for a wide range of different learning methods and datasets, the risk curve exhibits a double-descent (DD) trend as a function of the model size.
How many variables should be entered in a regression equation?
Leo Breiman and David Freedman · 1983
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Gaussian processes for machine learning
Christopher KI Williams and Carl Edward Rasmussen · 2006
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The lasso risk for gaussian matrices
Mohsen Bayati and Andrea Montanari · 2012
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A framework to characterize performance of lasso algorithms
Mihailo Stojnic · 2013
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Margins, shrinkage and boosting
Matus Telgarsky · 2013
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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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High dimensional robust m-estimation: Asymptotic variance via approximate message passing
David Donoho and Andrea Montanari · 2016
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High-dimensional dynamics of generalization error in neural networks
Madhu S Advani and Andrew M Saxe · 2017
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Asymptotic behavior of support vector machine for spiked population model
Hanwen Huang · 2017
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Reconciling modern machine learning and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2018
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Emmanuel J Candès and Pragya Sur · 2018
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Phase retrieval via polytope optimization: Geometry, phase transitions, and new algorithms
Oussama Dhifallah, Christos Thrampoulidis, and Yue M Lu · 2018
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On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators
Noureddine El Karoui · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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Precise error analysis of regularized m m -estimators in high dimensions
Christos Thrampoulidis, Ehsan Abbasi, and Babak Hassibi · 2018
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High-dimensional probability: An introduction with applications in data science
Roman Vershynin · 2018
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Universality in learning from linear measurements
Ehsan Abbasi, Fariborz Salehi, and Babak Hassibi · 2019
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Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, and Ji Xu · 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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The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei and Andrea Montanari · 2019
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Andrea Montanari, Feng Ruan, Youngtak Sohn, and Jun Yan · 2019
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Harmless interpolation of noisy data in regression
Vidya Muthukumar, Kailas Vodrahalli, and Anant Sahai · 2019
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More data can hurt for linear regression: Sample-wise double descent
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Peter L Bartlett, Philip M Long, Gábor Lugosi, and Alexander Tsigler · 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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Scaling description of generalization with number of parameters in deep learning
Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d’Ascoli, Giulio Biroli, Clément Hongler, and Matthieu Wyart · 2019
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Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
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The implicit bias of gradient descent on nonseparable data
Ziwei Ji and Matus Telgarsky · 2019
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On the precise error analysis of support vector machines
A. Kammoun and M.-S. Alouini · 2019
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Preetum Nakkiran · 2019
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Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever · 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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A jamming transition from under-to over-parametrization affects generalization in deep learning
S Spigler, M Geiger, S d’Ascoli, L Sagun, G Biroli, and M Wyart · 2019
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Sharp guarantees for solving random equations with one-bit information
Hossein Taheri, Ramtin Pedarsani, and Christos Thrampoulidis · 2019
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Does slope outperform bridge regression?
Shuaiwen Wang, Haolei Weng, and Arian Maleki · 2019
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How many variables should be entered in a principal component regression equation?
Ji Xu and Daniel Hsu · 2019
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Sharp asymptotics and optimal performance for inference in binary models
Hossein Taheri, Ramtin Pedarsani, and Christos Thrampoulidis · 2020
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