אריה קנטורוביץ

אקדמי בכיר

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

Daniel Berend,Aryeh Kontorovich, Lev Reyzin, Thomas Robinson

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.

שפת פרסום אנגלית
דפים 887-905
כתב עת Annals of Mathematics and Artificial Intelligence
כרך 88
נושא מספר 8
סטטוס פרסום פורסם - 01.08.2020

Keywords

Adversarial learning
Classification noise
Random walks

ASJC Scopus subject areas

Applied Mathematics
Artificial Intelligence
גישה למסמך
10.1007/s10472-020-09696-1
קבצים וקישורים אחרים
Link to publication in Scopus