# Communication Efficient Distributed Agnostic Boosting

arXiv: https://arxiv.org/abs/1506.06318 · HTML: https://mlsanity.com/paper/1506.06318/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2015.

## Built on by (most cited first, 6 of 6)

- [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)
- [Revisiting Agnostic Boosting](https://mlsanity.com/paper/2503.09384/) (2025)
- [What killed the Convex Booster ?](https://mlsanity.com/paper/2205.09628/) (2022)
- [A Resilient Distributed Boosting Algorithm](https://mlsanity.com/paper/2206.04713/) (2022)
- [Sample-Near-Optimal Agnostic Boosting with Improved Running Time](https://mlsanity.com/paper/2601.11265/) (2026)

## Abstract (arXiv)

We consider the problem of learning from distributed data in the agnostic setting, i.e., in the presence of arbitrary forms of noise. Our main contribution is a general distributed boosting-based procedure for learning an arbitrary concept space, that is simultaneously noise tolerant, communication efficient, and computationally efficient. This improves significantly over prior works that were either communication efficient only in noise-free scenarios or computationally prohibitive. Empirical results on large synthetic and real-world datasets demonstrate the effectiveness and scalability of the proposed approach.