Who Cited It

Reconciling modern machine-learning practice and the classical bias–variance trade-off

2019 · Proceedings of the National Academy of Sciences · 1,561 citations · 1 from inside this corpus

Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Reconciling modern machine-learning practice and the classical bias–variance trade-off (2019)Reconciling modern machine-le…The Nature of Statistical Learning Theory (1995)The Nature of Statistical Lea…Greedy function approximation: A gradient boosting machine. (2001)Greedy function approximation…Advances in Neural Information Processing Systems 19 (2007)Advances in Neural Informatio…Advances in neural information processing systems 7 (1996)Advances in neural informatio…Advances in Neural Information Processing Systems 29 (2016)Advances in Neural Informatio…A training algorithm for optimal margin classifiers (1992)A training algorithm for opti…Gaussian Processes in Machine Learning (2004)Gaussian Processes in Machine…Neural Networks and the Bias/Variance Dilemma (1992)Neural Networks and the Bias/…Random Features for Large-Scale Kernel Machines (2007)Random Features for Large-Sca…Understanding deep learning (still) requires rethinking generalization (2021)Understanding deep learning (…Boosting the margin: a new explanation for the effectiveness of voting methods (1998)Boosting the margin: a new ex…Understanding deep learning (still) requires rethinking generalization (2021)Understanding deep learning (…
12 of 12 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
The Nature of Statistical Learning Theory199539,409
Greedy function approximation: A gradient boosting machine.200130,137
Advances in Neural Information Processing Systems 19200715,653
Advances in neural information processing systems 7199614,393
Advances in Neural Information Processing Systems 29201613,474
A training algorithm for optimal margin classifiers199211,664
Gaussian Processes in Machine Learning20045,173
Neural Networks and the Bias/Variance Dilemma19923,612
Random Features for Large-Scale Kernel Machines20072,646
Understanding deep learning (still) requires rethinking generalization20212,370
Boosting the margin: a new explanation for the effectiveness of voting methods19982,344

What cites it, inside the corpus

PaperYearCited
Understanding deep learning (still) requires rethinking generalization20212,370

Links

DOI · Open access copy · OpenAlex record

Topics

Machine Learning and Data ClassificationComputer Science
Gaussian Processes and Bayesian InferenceComputer Science
Neural Networks and ApplicationsComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports43 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:57+00:00.

sha256 b3024609427ad450…