# Online Boosting with Bandit Feedback

arXiv: https://arxiv.org/abs/2007.11975 · HTML: https://mlsanity.com/paper/2007.11975/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2020.

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

- [Projection-free Adaptive Regret with Membership Oracles](https://mlsanity.com/paper/2211.12638/) (2022)
- [A Boosting Approach to Reinforcement Learning](https://mlsanity.com/paper/2108.09767/) (2021)
- [Boosting for Online Convex Optimization](https://mlsanity.com/paper/2102.09305/) (2021)
- [Multiclass Boosting: Simple and Intuitive Weak Learning Criteria](https://mlsanity.com/paper/2307.00642/) (2023)
- [Online Agnostic Multiclass Boosting](https://mlsanity.com/paper/2205.15113/) (2022)
- [Boosted Off-Policy Learning](https://mlsanity.com/paper/2208.01148/) (2022)
- [Lazy Queries Can Reduce Variance in Zeroth-order Optimization](https://mlsanity.com/paper/2206.07126/) (2022)

## Abstract (arXiv)

We consider the problem of online boosting for regression tasks, when only limited information is available to the learner. We give an efficient regret minimization method that has two implications: an online boosting algorithm with noisy multi-point bandit feedback, and a new projection-free online convex optimization algorithm with stochastic gradient, that improves state-of-the-art guarantees in terms of efficiency.