# Adversarial Online Learning with noise

arXiv: https://arxiv.org/abs/1810.09346 · HTML: https://mlsanity.com/paper/1810.09346/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Learning-to-learn non-convex piecewise-Lipschitz functions](https://mlsanity.com/paper/2108.08770/) (2021)
- [Learning on the Edge: Online Learning with Stochastic Feedback Graphs](https://mlsanity.com/paper/2210.04229/) (2022)
- [A CMDP-within-online framework for Meta-Safe Reinforcement Learning](https://mlsanity.com/paper/2405.16601/) (2024)
- [Non-stochastic Bandits With Evolving Observations](https://mlsanity.com/paper/2405.16843/) (2024)
- [Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons](https://mlsanity.com/paper/2110.04136/) (2021)
- [Online Learning with Uncertain Feedback Graphs](https://mlsanity.com/paper/2106.08441/) (2021)
- [Asymptotically-Optimal Gaussian Bandits with Side Observations](https://mlsanity.com/paper/2505.10698/) (2025)

## Abstract (arXiv)

We present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise rate and a variable noise rate. Our main results are tight regret bounds for learning with noise in the adversarial online learning model.