# A General Method for Robust Learning from Batches

arXiv: https://arxiv.org/abs/2002.11099 · HTML: https://mlsanity.com/paper/2002.11099/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [An Equivalence Between Data Poisoning and Byzantine Gradient Attacks](https://mlsanity.com/paper/2202.08578/) (2022)
- [Robust Estimation for Random Graphs](https://mlsanity.com/paper/2111.05320/) (2021)
- [Robust Testing and Estimation under Manipulation Attacks](https://mlsanity.com/paper/2104.10740/) (2021)
- [Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks](https://mlsanity.com/paper/2107.10302/) (2021)
- [Efficient List-Decodable Regression using Batches](https://mlsanity.com/paper/2211.12743/) (2022)
- [Robust estimation algorithms don't need to know the corruption level](https://mlsanity.com/paper/2202.05453/) (2022)
- [Linear Regression using Heterogeneous Data Batches](https://mlsanity.com/paper/2309.01973/) (2023)
- [FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data](https://mlsanity.com/paper/2106.11732/) (2021)

## Abstract (arXiv)

In many applications, data is collected in batches, some of which are corrupt or even adversarial. Recent work derived optimal robust algorithms for estimating discrete distributions in this setting. We consider a general framework of robust learning from batches, and determine the limits of both classification and distribution estimation over arbitrary, including continuous, domains. Building on these results, we derive the first robust agnostic computationally-efficient learning algorithms for piecewise-interval classification, and for piecewise-polynomial, monotone, log-concave, and gaussian-mixture distribution estimation.