# Robust Risk Minimization for Statistical Learning

arXiv: https://arxiv.org/abs/1910.01544 · HTML: https://mlsanity.com/paper/1910.01544/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Iterative thresholding for non-linear learning in the strong varepsilon-contamination model](https://mlsanity.com/paper/2409.03703/) (2024)

## Abstract (arXiv)

We consider a general statistical learning problem where an unknown fraction of the training data is corrupted. We develop a robust learning method that only requires specifying an upper bound on the corrupted data fraction. The method minimizes a risk function defined by a non-parametric distribution with unknown probability weights. We derive and analyse the optimal weights and show how they provide robustness against corrupted data. Furthermore, we give a computationally efficient coordinate descent algorithm to solve the risk minimization problem. We demonstrate the wide range applicability of the method, including regression, classification, unsupervised learning and classic parameter estimation, with state-of-the-art performance.