The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficient noise-tolerant learning from statistical queries
Open accessAbstract
Article Free Access Share on Efficient noise-tolerant learning from statistical queries Author: Michael Kearns View Profile Authors Info & Claims STOC '93: Proceedings of the twenty-fifth annual ACM symposium on Theory of ComputingJune 1993Pages 392–401https://doi.org/10.1145/167088.167200Published:01 June 1993Publication History 139citation502DownloadsMetricsTotal Citations139Total Downloads502Last 12 Months37Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Introduction to Computational Learning Theory
Kearns, M. J., & Vazirani, U. (1994) · doi:10.7551/mitpress/3897.001.0001 · Cited by 1,697
Self-citation
Cited by
Cryptographic Primitives Based on Hard Learning Problems
Blum, A., Furst, M., Kearns, M., & Lipton, R. J. (1994) · Lecture Notes in Computer Science · doi:10.1007/3-540-48329-2_24 · Cited by 332
Self-citationSimple and Scalable Response Prediction for Display Advertising
Chapelle, O., Manavoglu, E., & Rosales, R. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2532128 · Cited by 318
On the learnability of discrete distributions
Kearns, M., Mansour, Y., Ron, D., Rubinfeld, R., Schapire, R. E., & Sellie, L. (1994) · Proceedings of the twenty-sixth annual ACM symposium on Theory of computing - STOC '94 · doi:10.1145/195058.195155 · Cited by 273
Self-citationDistributed Differential Privacy via Shuffling
Cheu, A., Smith, A., Ullman, J., Zeber, D., & Zhilyaev, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-17653-2_13 · Cited by 268
Metadata
- Authors
- Kearns, M.
- Venue
- Proceedings of the twenty-fifth annual ACM symposium on Theory of computing - STOC '93
- Publisher
- ACM Press
- Published
- 1993
- Pages
- 392-401
- OpenAlex
- W2072631813
- Cited by
- 173
- References
- 1
- FWCI
- 11.42
- Type
- conference paper
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Efficient noise-tolerant learning from statistical queries. https://app.cochat.ai/papers/10.1145/167088.167200
- The paper
- Kearns, M. (1993). Efficient noise-tolerant learning from statistical queries. Proceedings of the twenty-fifth annual ACM symposium on Theory of computing - STOC '93. https://doi.org/10.1145/167088.167200
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
