On biased random walks, corrupted intervals, and learning under adversarial design
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE(2020)
摘要
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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关键词
Random walks,Classification noise,Adversarial learning
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