# Online Agnostic Multiclass Boosting

arXiv: https://arxiv.org/abs/2205.15113 · HTML: https://mlsanity.com/paper/2205.15113/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 9 of 9)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Online Gradient Boosting](https://mlsanity.com/paper/1506.04820/) (2015)
- [Optimal and Adaptive Algorithms for Online Boosting](https://mlsanity.com/paper/1502.02651/) (2015)
- [Online Agnostic Boosting via Regret Minimization](https://mlsanity.com/paper/2003.01150/) (2020)
- [Online Multiclass Boosting](https://mlsanity.com/paper/1702.07305/) (2017)
- [Boosting for Control of Dynamical Systems](https://mlsanity.com/paper/1906.08720/) (2019)
- [Online Boosting with Bandit Feedback](https://mlsanity.com/paper/2007.11975/) (2020)
- [A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness](https://mlsanity.com/paper/2103.01276/) (2021)

## Built on by (most cited first, 3 of 3)

- [On Classification-Calibration of Gamma-Phi Losses](https://mlsanity.com/paper/2302.07321/) (2023)
- [Sample-Efficient Agnostic Boosting](https://mlsanity.com/paper/2410.23632/) (2024)
- [Sample-Optimal Agnostic Boosting with Unlabeled Data](https://mlsanity.com/paper/2503.04706/) (2025)

## Abstract (arXiv)

Boosting is a fundamental approach in machine learning that enjoys both strong theoretical and practical guarantees. At a high-level, boosting algorithms cleverly aggregate weak learners to generate predictions with arbitrarily high accuracy. In this way, boosting algorithms convert weak learners into strong ones. Recently, Brukhim et al. extended boosting to the online agnostic binary classification setting. A key ingredient in their approach is a clean and simple reduction to online convex optimization, one that efficiently converts an arbitrary online convex optimizer to an agnostic online booster. In this work, we extend this reduction to multiclass problems and give the first boosting algorithm for online agnostic mutliclass classification. Our reduction also enables the construction of algorithms for statistical agnostic, online realizable, and statistical realizable multiclass boosting.