The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Learning in the Presence of Malicious Errors
Abstract
In this paper an extension of the distribution-free model of learning introduced by Valiant [Comm. ACM, 27(1984), pp. 1134–1142] that allows the presence of malicious errors in the examples given to a learning algorithm is studied. Such errors are generated by an adversary with unbounded computational power and access to the entire history of the learning algorithm’s computation. Thus, a worst-case model of errors is studied. The results of this research include general methods for bounding the rate of error tolerable by any learning algorithm, efficient algorithms tolerating nontrivial rates of malicious errors, and equivalences between problems of learning with errors and standard combinatorial optimization problems.
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
A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations
Chernoff, H. (1952) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729330 · Cited by 3,550
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
A Greedy Heuristic for the Set-Covering Problem
Chvatal, V. (1979) · Mathematics of Operations Research · doi:10.1287/moor.4.3.233 · Cited by 2,581
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
Vapnik, V. N., & Chervonenkis, A. Y. (1971) · Theory of Probability & Its Applications · doi:10.1137/1116025 · Cited by 2,448
Approximation algorithms for combinatorial problems
Johnson, D. S. (1974) · Journal of Computer and System Sciences · doi:10.1016/s0022-0000(74)80044-9 · Cited by 2,197
Learnability and the Vapnik-Chervonenkis dimension
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1989) · Journal of the ACM · doi:10.1145/76359.76371 · Cited by 1,839
On the ratio of optimal integral and fractional covers
Lovász, L. (1975) · Discrete Mathematics · doi:10.1016/0012-365x(75)90058-8 · Cited by 1,114
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987) · Information Processing Letters · doi:10.1016/0020-0190(87)90114-1 · Cited by 1,054
Rivest, R. L. (1987) · Machine Learning · doi:10.1007/bf00058680 · Cited by 886
Angluin, D., & Laird, P. (1988) · Machine Learning · doi:10.1007/bf00116829 · Cited by 827
Cited by
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
Can machine learning be secure?
Barreno, M., Nelson, B., Sears, R., Joseph, A. D., & Tygar, J. D. (2006) · Proceedings of the 2006 ACM Symposium on Information, computer and communications security · doi:10.1145/1128817.1128824 · Cited by 836
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2018.00057 · Cited by 861
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., & Roli, F. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3128572.3140451 · Cited by 549
SoK: Security and Privacy in Machine Learning
Papernot, N., McDaniel, P., Sinha, A., & Wellman, M. P. (2018) · 2018 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2018.00035 · Cited by 502
Metadata
- Authors
- Kearns, M., & Li, M.
- Venue
- SIAM Journal on Computing
- Published
- 1993-08
- Volume
- 22
- Issue
- 4
- Pages
- 807-837
- ISSN
- 0097-5397 (ISSN-L)
- 1095-7111
- OpenAlex
- W1968998685
- Cited by
- 340
- References
- 13
- FWCI
- 13.82
- Type
- journal article
- Language
- en
- Open access
- closed
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: Learning in the Presence of Malicious Errors. https://app.cochat.ai/papers/10.1137/0222052
- The paper
- Kearns, M., & Li, M. (1993). Learning in the Presence of Malicious Errors. SIAM Journal on Computing. https://doi.org/10.1137/0222052
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.
