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Self bounding learning algorithms

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Yoav FreundAuthors Info & Claims

COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory

Pages 247 - 258

https://doi.org/10.1145/279943.279993

Published: 24 July 1998 Publication History

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Cited By

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Viallard PGermain PHabrard AMorvant E(2023)A general framework for the practical disintegration of PAC-Bayesian boundsMachine Learning10.1007/s10994-023-06391-0113:2(519-604)Online publication date: 11-Oct-2023
https://doi.org/10.1007/s10994-023-06391-0
Sicilia AZhao XSosnovskikh AHwang S(2021)PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical ImagingMedical Image Computing and Computer Assisted Intervention – MICCAI 202110.1007/978-3-030-87199-4_53(560-570)Online publication date: 21-Sep-2021
https://doi.org/10.1007/978-3-030-87199-4_53
Viallard PGermain PHabrard AMorvant E(2021)Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-BoundMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-030-86520-7_11(167-183)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-3-030-86520-7_11
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Index Terms

Self bounding learning algorithms
1. Computing methodologies
  1. Machine learning
2. Theory of computation
  1. Computational complexity and cryptography
    1. Complexity classes

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Published In

COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory

July 1998

304 pages

ISBN:1581130570

DOI:10.1145/279943

Chairmen:
Peter Bartlett
Australian National Univ., Australia
,
Yishay Mansour
Tel-Aviv Univ., Israel

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 July 1998

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COLT98: The 11th Annual Conference on Computational Learning Theroy

July 24 - 26, 1998

Wisconsin, Madison, USA

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Viallard PGermain PHabrard AMorvant E(2023)A general framework for the practical disintegration of PAC-Bayesian boundsMachine Learning10.1007/s10994-023-06391-0113:2(519-604)Online publication date: 11-Oct-2023
https://doi.org/10.1007/s10994-023-06391-0
Sicilia AZhao XSosnovskikh AHwang S(2021)PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical ImagingMedical Image Computing and Computer Assisted Intervention – MICCAI 202110.1007/978-3-030-87199-4_53(560-570)Online publication date: 21-Sep-2021
https://doi.org/10.1007/978-3-030-87199-4_53
Viallard PGermain PHabrard AMorvant E(2021)Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-BoundMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-030-86520-7_11(167-183)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-3-030-86520-7_11
Rivasplata OKuzborskij ISzepesvari CShawe-Taylor JLarochelle HRanzato MHadsell RBalcan MLin H(2020)PAC-Bayes analysis beyond the usual boundsProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3497136(16833-16845)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.5555/3495724.3497136
Rivasplata OParrado-Hernández EShawe-Taylor JSun SSzepesvári C(2018)PAC-Bayes bounds for stable algorithms with instance-dependent priorsProceedings of the 32nd International Conference on Neural Information Processing Systems10.5555/3327546.3327595(9234-9244)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.5555/3327546.3327595
Atzmueller M(2015)Subgroup discoveryWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11445:1(35-49)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1002/widm.1144
Shah M(2007)Sample compression bounds for decision treesProceedings of the 24th international conference on Machine learning10.1145/1273496.1273597(799-806)Online publication date: 20-Jun-2007
https://dl.acm.org/doi/10.1145/1273496.1273597
Lozano FKoltchinskii V(2005)Self bounding Genetic Algorithms for Machine LearningProceedings of the Fourth International Conference on Machine Learning and Applications10.1109/ICMLA.2005.57(343-350)Online publication date: 15-Dec-2005
https://dl.acm.org/doi/10.1109/ICMLA.2005.57
Boucheron SBousquet OLugosi G(2005)Theory of Classification: a Survey of Some Recent AdvancesESAIM: Probability and Statistics10.1051/ps:20050189(323-375)Online publication date: 15-Nov-2005
https://doi.org/10.1051/ps:2005018
Langford JMcAllester D(2004)Computable Shell Decomposition BoundsThe Journal of Machine Learning Research10.5555/1005332.10053515(529-547)Online publication date: 1-Dec-2004
https://dl.acm.org/doi/10.5555/1005332.1005351
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Microchoice Bounds and Self Bounding Learning Algorithms

Multiview self-learning

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Microchoice Bounds and Self Bounding Learning Algorithms

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Bounding Average-Energy Games

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