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``Benign overfitting'', the ability of certain algorithms to interpolate noisy training data and yet perform well out-of-sample, has been a topic of considerable recent interest.
Properties of multivariate cauchy and poly-cauchy distributions with bayesian g-prior applications
Guorui Bian and James M Dickey · 1991
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The hilbert kernel regression estimate
Luc Devroye, Laszlo Györfi, and Adam Krzyżak · 1998
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Directional statistics
Kanti V Mardia, Peter E Jupp, and KV Mardia · 2000
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Analysis
Elliott H Lieb and Michael Loss · 2001
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A generalized representer theorem
Bernhard Schölkopf, Ralf Herbrich, and Alex J Smola · 2001
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A distribution-free theory of nonparametric regression
László Györfi, Michael Kohler, Adam Krzyzak, Harro Walk, et al · 2002
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In search of the real inductive bias: On the role of implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014
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Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate
Mikhail Belkin, Daniel J Hsu, and Partha Mitra · 2018
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To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, and Soumik Mandal · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Bernhard Scholkopf and Alexander J Smola · 2018
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
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Does data interpolation contradict statistical optimality?
Mikhail Belkin, Alexander Rakhlin, and Alexandre B Tsybakov · 2019
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Consistency of interpolation with laplace kernels is a high-dimensional phenomenon
Alexander Rakhlin and Xiyu Zhai · 2019
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Benign overfitting in linear regression
Peter L Bartlett, Philip M Long, Gábor Lugosi, and Alexander Tsigler · 2020
Harmless interpolation of noisy data in regression
Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian, and Anant Sahai · 2020
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How rotational invariance of common kernels prevents generalization in high dimensions
Konstantin Donhauser, Mingqi Wu, and Fanny Yang · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2022
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Benign, tempered, or catastrophic: A taxonomy of overfitting
Neil Mallinar, James B Simon, Amirhesam Abedsoltan, Parthe Pandit, Mikhail Belkin, and Preetum Nakkiran · 2022
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Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, and Ji Xu · 2020
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Just interpolate: Kernel “ridgeless” regression can generalize
Tengyuan Liang and Alexander Rakhlin · 2020
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Generalization error of random feature and kernel methods: hypercontractivity and kernel matrix concentration
Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2022
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The generalization error of random features regression: Precise asymptotics and the double descent curve
Song Mei and Andrea Montanari · 2022
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Feature learning in neural networks and kernel machines that recursively learn features
Adityanarayanan Radhakrishnan, Daniel Beaglehole, Parthe Pandit, and Mikhail Belkin · 2022
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