# On Biased Random Walks, Corrupted Intervals, and Learning Under Adversarial Design

arXiv: https://arxiv.org/abs/2003.13561 · HTML: https://mlsanity.com/paper/2003.13561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [On the Adversarial Robustness of Benjamini Hochberg](https://mlsanity.com/paper/2501.03402/) (2025)

## Abstract (arXiv)

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.