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Decision theoretic generalizations of the PAC model for neural net and other learning applications

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David HausslerAuthors Info & Claims

Information and Computation, Volume 100, Issue 1

Pages 78 - 150

https://doi.org/10.1016/0890-5401(92)90010-D

Published: 01 September 1992 Publication History

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Diakonikolas IKane DKontonis VLiu SZarifis NMohar BShinkar IO'Donnell R(2024)Super Non-singular Decompositions of Polynomials and Their Application to Robustly Learning Low-Degree PTFsProceedings of the 56th Annual ACM Symposium on Theory of Computing10.1145/3618260.3649776(152-159)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3618260.3649776
Hadiashar SNayak ASinha P(2024)Optimal Lower Bounds for Quantum Learning via Information TheoryIEEE Transactions on Information Theory10.1109/TIT.2023.332452770:3(1876-1896)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TIT.2023.3324527
Ben-David SBie AKamath GLechner TOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Distribution learnability and robustnessProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668418(52732-52758)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668418
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Agnostic PAC learning of functions on analog neural nets

We consider learning on multilayer neural nets with piecewise polynomial activation functions and a fixed number k of numerical inputs. We exhibit arbitrarily large network architectures for which efficient and provably successful learning algorithms exist ...
Perceptron, Winnow, and PAC Learning

We analyze the performance of the widely studied Perceptron and Winnow algorithms for learning linear threshold functions under Valiant's probably approximately correct (PAC) model of concept learning. We show that under the uniform distribution on ...
DECISION THEORETIC GENERALIZATIONS OF THE PAC MODEL FORNEURAL NET AND OTHER LEARNING APPLICATIONS

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Information and Computation Volume 100, Issue 1

Sept. 1992

150 pages

ISSN:0890-5401

Editor:
Albert R. Meyer
Massachusetts Institute of Technology, Cambridge

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Academic Press, Inc.

United States

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Published: 01 September 1992

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Diakonikolas IKane DKontonis VLiu SZarifis NMohar BShinkar IO'Donnell R(2024)Super Non-singular Decompositions of Polynomials and Their Application to Robustly Learning Low-Degree PTFsProceedings of the 56th Annual ACM Symposium on Theory of Computing10.1145/3618260.3649776(152-159)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3618260.3649776
Hadiashar SNayak ASinha P(2024)Optimal Lower Bounds for Quantum Learning via Information TheoryIEEE Transactions on Information Theory10.1109/TIT.2023.332452770:3(1876-1896)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TIT.2023.3324527
Ben-David SBie AKamath GLechner TOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Distribution learnability and robustnessProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668418(52732-52758)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668418
Gopalan PKim MReingold OOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Swap agnostic learning, or characterizing omniprediction via multicalibrationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667858(39936-39956)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667858
Diakonikolas IKane DKontonis VLiu SZarifis NOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Efficient testable learning of halfspaces with adversarial label noiseProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667836(39470-39490)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667836
Wang PZarifis NDiakonikolas IDiakonikolas JKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Robustly learning a single neuron via sharpnessProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619928(36541-36577)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619928
Diakonikolas IKane DRen LKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Near-optimal cryptographic hardness of agnostically learning halfspaces and ReLU regression under Gaussian marginalsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618722(7922-7938)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3618722
Agrawal AInamdar TSaurabh SXue JWilliams BChen YNeville J(2023)Clustering what mattersProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i6.25818(6666-6674)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i6.25818
Rubinfeld RVasilyan ASaha BServedio R(2023)Testing Distributional Assumptions of Learning AlgorithmsProceedings of the 55th Annual ACM Symposium on Theory of Computing10.1145/3564246.3585117(1643-1656)Online publication date: 2-Jun-2023
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Chamon LPaternain SCalvo-Fullana MRibeiro A(2023)Constrained Learning With Non-Convex LossesIEEE Transactions on Information Theory10.1109/TIT.2022.318794869:3(1739-1760)Online publication date: 1-Mar-2023
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Agnostic PAC learning of functions on analog neural nets

Perceptron, Winnow, and PAC Learning

DECISION THEORETIC GENERALIZATIONS OF THE PAC MODEL FORNEURAL NET AND OTHER LEARNING APPLICATIONS

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