Article

Free access

Efficient noise-tolerant learning from statistical queries

Author:

Michael KearnsAuthors Info & Claims

STOC '93: Proceedings of the twenty-fifth annual ACM symposium on Theory of Computing

Pages 392 - 401

https://doi.org/10.1145/167088.167200

Published: 01 June 1993 Publication History

PDF eReader

References

[1]

Dana Angluin and Philip Laird. Learning from noisy examples. Machine Learning, 2(4):343-370, 1988.]]

Digital Library

Google Scholar

[2]

Eric B. Baum and Yuh-Dauh Lyuu. The transition to perfect generalization in perceptrons. Neural Computation, 3:386- 401, 1991.]]

Crossref

Google Scholar

[3]

Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K. Warmuth. Learnability and the Vapnik- Chervonenkis dimension. Journal of the Association for Computing Machinery, 36(4):929-965, October 1989.]]

Digital Library

Google Scholar

[4]

A. Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant. A general lower bound on the number of examples needed for learning. In First Workshop on Computatinal Learning Theor//, pages 139-154, Cambridge, Mass. August 1988. Morgan Kaufmann.]]

Digital Library

Google Scholar

[5]

Merrick L. Furst, Jeffrey C. Jackson, and Sean W. Smith. Improved learning of AC0 functions. In Proceedings of the Fourth Annual Workshop on Computational Learning Theory, pages 317-325, August 1991.]]

Digital Library

Google Scholar

[6]

E. Gardner and B. Derrida. Three unfinished works on the optimal storage capacity of networks. 3. Phys. A: Math. Gen., 22:1983-1994, 1989.]]

Crossref

Google Scholar

[7]

Thomas Hancock and Yishay Mansour. Learning monotone k# DNF formulas on product distributions. In Proceedings of the Fourth Annual Workshop on Computational Learning Theory, pages 179-183, August 1991.]]

Digital Library

Google Scholar

[8]

David Haussler. Quantifying inductive bias: AI learning algorithms and Valiant's learning framework. Artificial Intelligence, 36:177-221, 1988.]]

Digital Library

Google Scholar

[9]

David Helmbold, Robert Sloan, and Manfred K. Warmuth. Learning integer lattices. SIAM Journal on Computing, 21(2):240-266, 1992.]]

Digital Library

Google Scholar

[10]

Michael Kearns. The Computational Complexity of MacJ#ine Learning. The MIT Press, 1990.]]

Digital Library

Google Scholar

[11]

Michael Kearns and Ming Li. Learning in the presence of malicious errors. In Proceedings of the Twentieth Annual A CM Symposium on Theory of Computing, pages 267-280, May 1988. To appear, SIAM Journal on Computing.]]

Digital Library

Google Scholar

[12]

Michael Kearns, Ming Li, Leonard Pitt, and Leslie Valiant. On the learnability of Boolean formulae. In Proceedings of the Nineteenth Annual A CM Symposium on Theory of Computing, pages 285-295, May 1987.]]

Digital Library

Google Scholar

[13]

Michael Kearns and Leonard Pitt. A polynomial-time algorithm for learning k-variable pattern languages from examples, in Proceedings of the Second Annual Workshop on Computational Learning Theory, pages 57-71, July 1989.]]

Digital Library

Google Scholar

[14]

Michael J. Kearns and Robert E. Schapire. Efficient distribution-free learning of probabilistic concepts. In 31st Annual Symposium on Foundations of Computer Science, pages 382-391, October 1990. To appear, Journal of Computer and System Sciences.]]

Digital Library

Google Scholar

[15]

Philip D. Laird. Learning from Good and Bad Data. Kluwer international series in engineering and computer science. Kluwer Academic Publishers, Boston, 1988.]]

Digital Library

Google Scholar

[16]

Nathan Linial, Yishay Mansour, and Noam Nisan. Constant depth circuits, Fourier transform, and learnability. In 6!Oth Annual Symposium on Foundations of Computer Science, pages 574-579, October 1989.]]

Digital Library

Google Scholar

[17]

Marvin Minsky and Seymour Papert. Perceptrons: An Introduction to Computational Geometry (Expanded Edition). The MIT Press, 1988.]]

Digital Library

Google Scholar

[18]

Leonard Pitt and Leslie G. Valiant. Computational limitations on learning from examples. Journal of the Association for Computing Machinery, 35(4):965-984, October 1988.]]

Digital Library

Google Scholar

[19]

Ronald L. Rivest. Learning decision lists. Machine Learning, 2(3):229-246, 1987.]]

Digital Library

Google Scholar

[20]

Yasubumi Sakakibara. Algorithmic Learning of Formal Languages and Decision Trees. PhD thesis, Tokyo Institute of Technology, October 1991. Research Report IIAS-RR-91- 22E, International Institute for Advanced Study of Social Information Science, Fujitsu Laboratories, Ltd.]]

Google Scholar

[21]

Robert E. Schapire. Learning probabilistic read-once formulas on product distributions. In Proceedings of the Fourth Annual Workshop on Computational Learning Theory, August 1991. To appear, Machine Learning.]]

Digital Library

Google Scholar

[22]

Robert Elias Schapire. The Design and Analysis of Efficient Learning Algorithms. The MIT Press, 1992.]]

Digital Library

Google Scholar

[23]

H.S. Seung, H. Sompolinsky, and N. Tishby. Statistical mechanics of learning from examples. Physical Review A, 45(8):6056-6091, April 1992.]]

Crossref

Google Scholar

[24]

Robert H. Sloan. Types of noise in data for concept learning. In Proceedings of the 1988 Workshop on Computational Learning Theory, pages 91-96, August 1988.]]

Digital Library

Google Scholar

[25]

L. G. Valiant. A theory of the learnable. Communications o# the ACM, 27(11):1134-1142, November 1984.]]

Digital Library

Google Scholar

[26]

L. G. Valiant. Learning disjunctions of conjunctions. In Proceedings of the 9th International Joint Conference on Artificial Intelligence, pages 560-566, August 1985.]]

Digital Library

Google Scholar

[27]

V. N. Vapnik and A. Ya. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its applications, XVI(2):264- 280, 1971.]]

Crossref

Google Scholar

Cited By

View all

Filmus YIshai YKaplan AKindler G(2024)Limits of PreprocessingComputational Complexity10.1007/s00037-024-00251-633:1Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1007/s00037-024-00251-6
Alimanov KAssylbekov Z(2023)Hardness of Learning AES with Gradient-Based MethodsCryptology and Network Security10.1007/978-981-99-7563-1_6(126-133)Online publication date: 31-Oct-2023
https://doi.org/10.1007/978-981-99-7563-1_6
Cheu ASmith AUllman J(2021)Manipulation Attacks in Local Differential Privacy2021 IEEE Symposium on Security and Privacy (SP)10.1109/SP40001.2021.00001(883-900)Online publication date: May-2021
https://doi.org/10.1109/SP40001.2021.00001
Show More Cited By

Index Terms

Efficient noise-tolerant learning from statistical queries
1. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Statistical graphics

Recommendations

Efficient noise-tolerant learning from statistical queries

In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” learning algorithms in the most general way, we formalize ...
On the Efficiency of Noise-Tolerant PAC Algorithms Derived from Statistical Queries

The Statistical Query (SQ) model provides an elegant means for generating noise-tolerant PAC learning algorithms that run in time inverse polynomial in the noise rate. Whether or not there is an SQ algorithm for every noise-tolerant PAC algorithm that ...
PAC Learning from Positive Statistical Queries
ALT '98: Proceedings of the 9th International Conference on Algorithmic Learning Theory

Learning from positive examples occurs very frequently in natural learning. The PAC learning model of Valiant takes many features of natural learning into account, but in most cases it fails to describe such kind of learning. We show that in order to ...

Comments

Information & Contributors

Information

Published In

STOC '93: Proceedings of the twenty-fifth annual ACM symposium on Theory of Computing

June 1993

812 pages

ISBN:0897915917

DOI:10.1145/167088

Chairmen:
Rao Kosaraju
John Hopkins Univ., Baltimore, MD
,
David Johnson
AT&T Bell Lab.
,
Alok Aggarwal
IBM T. J. Watson Research Center,Yorktown Heights, NY

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1993

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

STOC93

Sponsor:

SIGACT

STOC93: 25th Annual ACM Symposium on the Theory of Computing

May 16 - 18, 1993

California, San Diego, USA

Acceptance Rates

Overall Acceptance Rate 1,469 of 4,586 submissions, 32%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

140
Total Citations
View Citations
519
Total Downloads

Downloads (Last 12 months)37
Downloads (Last 6 weeks)2

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Filmus YIshai YKaplan AKindler G(2024)Limits of PreprocessingComputational Complexity10.1007/s00037-024-00251-633:1Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1007/s00037-024-00251-6
Alimanov KAssylbekov Z(2023)Hardness of Learning AES with Gradient-Based MethodsCryptology and Network Security10.1007/978-981-99-7563-1_6(126-133)Online publication date: 31-Oct-2023
https://doi.org/10.1007/978-981-99-7563-1_6
Cheu ASmith AUllman J(2021)Manipulation Attacks in Local Differential Privacy2021 IEEE Symposium on Security and Privacy (SP)10.1109/SP40001.2021.00001(883-900)Online publication date: May-2021
https://doi.org/10.1109/SP40001.2021.00001
Yan DYu YLiu HZhao SZhang J(2021)An improved algorithm for learning sparse parities in the presence of noiseTheoretical Computer Science10.1016/j.tcs.2021.04.026873(76-86)Online publication date: Jun-2021
https://doi.org/10.1016/j.tcs.2021.04.026
Gao WZhang TYang BZhou Z(2021)On the noise estimation statisticsArtificial Intelligence10.1016/j.artint.2021.103451293(103451)Online publication date: Apr-2021
https://doi.org/10.1016/j.artint.2021.103451
Xia XLiu THan BWang NGong MLiu HNiu GTao DSugiyama MLarochelle HRanzato MHadsell RBalcan MLin H(2020)Part-dependent label noiseProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3496361(7597-7610)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.5555/3495724.3496361
Filmus YIshai YKaplan AKindler GAceto L(2020)Limits of preprocessingProceedings of the 35th Computational Complexity Conference10.4230/LIPIcs.CCC.2020.17(1-22)Online publication date: 28-Jul-2020
https://dl.acm.org/doi/10.4230/LIPIcs.CCC.2020.17
Chan TKahng AWoo MKahng A(2020)Revisiting inherent noise floors for interconnect predictionProceedings of the Workshop on System-Level Interconnect: Problems and Pathfinding Workshop10.1145/3414622.3431907(1-7)Online publication date: 5-Nov-2020
https://dl.acm.org/doi/10.1145/3414622.3431907
Burstein WWilmes J(2020)Obstacles to Depth Compression of Neural NetworksArtificial Neural Networks and Machine Learning – ICANN 202010.1007/978-3-030-61616-8_9(104-115)Online publication date: 15-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-61616-8_9
Diakonikolas IGouleakis TTzamos CWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Distribution-independent PAC learning of halfspaces with massart noiseProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454714(4749-4760)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3454714
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Efficient noise-tolerant learning from statistical queries

On the Efficiency of Noise-Tolerant PAC Algorithms Derived from Statistical Queries

PAC Learning from Positive Statistical Queries