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The over-parameterized models attract much attention in the era of data science and deep learning.
Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, and Ji Xu · 1903
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che-hui Lien · 2009
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Optimal weighted nearest neighbour classifiers
Richard J Samworth et al · 2012
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Rates of convergence for nearest neighbor classification
Kamalika Chaudhuri and Sanjoy Dasgupta · 2014
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Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach
Robert J Lyon, BW Stappers, S Cooper, JM Brooke, and JD Knowles · 2016
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Stabilized nearest neighbor classifier and its statistical properties
Will Wei Sun, Xingye Qiao, and Guang Cheng · 2016
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Local nearest neighbour classification with applications to semi-supervised learning
Timothy I Cannings, Thomas B Berrett, and Richard J Samworth · 2017
Cited alongside, same era.
Explaining the success of adaboost and random forests as interpolating classifiers
Abraham J Wyner, Matthew Olson, Justin Bleich, and David Mease · 2017
Cited alongside, same era.
Diverse neural network learns true target functions
Bo Xie, Yingyu Liang, and Le Song · 2017
Cited alongside, same era.
On the optimization of deep networks: Implicit acceleration by overparameterization
Sanjeev Arora, Nadav Cohen, and Elad Hazan · 2018
Cited alongside, same era.
Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
Later among the works it cites.
Analyzing the robustness of nearest neighbors to adversarial examples
Yizhen Wang, Somesh Jha, and Kamalika Chaudhuri · 2018
Later among the works it cites.
Statistical optimality of interpolated nearest neighbor algorithms
Yue Xing, Qifan Song, and Guang Cheng · 2018
Later among the works it cites.
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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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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Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate
Mikhail Belkin, Daniel Hsu, and Partha Mitra · 2018
Cited alongside, same era.
On the power of over-parametrization in neural networks with quadratic activation
Simon S Du and Jason D Lee · 2018
Cited alongside, same era.
Reconciling modern machine learning and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal
Cited in the paper.
Does data interpolation contradict statistical optimality?
Mikhail Belkin, Alexander Rakhlin, and Alexandre B Tsybakov
Cited in the paper.
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Gradient descent finds global minima of deep neural networks
Simon S Du, Jason D Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 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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