## Learning in the Presence of Malicious Errors (1993)

Venue: | SIAM Journal on Computing |

Citations: | 169 - 12 self |

### BibTeX

@ARTICLE{Kearns93learningin,

author = {Michael Kearns and Ming Li},

title = {Learning in the Presence of Malicious Errors},

journal = {SIAM Journal on Computing},

year = {1993},

volume = {22},

pages = {807--837}

}

### Years of Citing Articles

### OpenURL

### Abstract

In this paper we study an extension of the distribution-free model of learning introduced by Valiant [23] (also known as the probably approximately correct or PAC model) that allows the presence of malicious errors in the examples given to a learning algorithm. Such errors are generated by an adversary with unbounded computational power and access to the entire history of the learning algorithm's computation. Thus, we study a worst-case model of errors. Our results 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. 1 Introduction In this paper, we study a practical extension to Valiant's distribution-free model of learning: the presence of errors (possibly maliciously generated by an adversary) in the sample data. The distribution-free model typically makes the idealize...

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