Daniel Berend
Senior Academic
On biased random walks, corrupted intervals, and learning under adversarial design
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.
| Publication language | English |
| Pages | 887-905 |
| Journal | Annals of Mathematics and Artificial Intelligence |
| Volume | 88 |
| Issue number | 8 |
| Publication status | Published - 01.08.2020 |
Keywords
Adversarial learning
Classification noise
Random walks
ASJC Scopus subject areas
Applied Mathematics
Artificial Intelligence
