On biased random walks, corrupted intervals, and learning under adversarial design

Daniel Berend; Aryeh Kontorovich; Lev Reyzin; Thomas Robinson

doi:10.1007/s10472-020-09696-1

On biased random walks, corrupted intervals, and learning under adversarial design

Daniel Berend, Aryeh Kontorovich, Lev Reyzin, Thomas Robinson

Department of Computer Science

Ben-Gurion University of the Negev

Research output: Contribution to journal › Article › peer-review

Abstract

We tackle some fundamental problems in probability theory on corrupted random processes on the integer line. We analyze when a biased random walk is expected to reach its bottommost point and when intervals of integer points can be detected under a natural model of noise. We apply these results to problems in learning thresholds and intervals under a new model for learning under adversarial design.

Original language	American English
Pages (from-to)	887-905
Number of pages	19
Journal	Annals of Mathematics and Artificial Intelligence
Volume	88
Issue number	8
DOIs	https://doi.org/10.1007/s10472-020-09696-1
State	Published - 1 Aug 2020

Keywords

Adversarial learning
Classification noise
Random walks

All Science Journal Classification (ASJC) codes

Applied Mathematics
Artificial Intelligence

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10.1007/s10472-020-09696-1

Cite this

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title = "On biased random walks, corrupted intervals, and learning under adversarial design",

abstract = "We tackle some fundamental problems in probability theory on corrupted random processes on the integer line. We analyze when a biased random walk is expected to reach its bottommost point and when intervals of integer points can be detected under a natural model of noise. We apply these results to problems in learning thresholds and intervals under a new model for learning under adversarial design.",

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AB - We tackle some fundamental problems in probability theory on corrupted random processes on the integer line. We analyze when a biased random walk is expected to reach its bottommost point and when intervals of integer points can be detected under a natural model of noise. We apply these results to problems in learning thresholds and intervals under a new model for learning under adversarial design.

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On biased random walks, corrupted intervals, and learning under adversarial design

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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