# Adaptive Robust Learning using Latent Bernoulli Variables

arXiv: https://arxiv.org/abs/2312.00585 · HTML: https://mlsanity.com/paper/2312.00585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [SELF: Learning to Filter Noisy Labels with Self-Ensembling](https://mlsanity.com/paper/1910.01842/) (2019)

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

- [Bayesian Robust Aggregation for Federated Learning](https://mlsanity.com/paper/2505.02490/) (2025)

## Abstract (arXiv)

We present an adaptive approach for robust learning from corrupted training sets. We identify corrupted and non-corrupted samples with latent Bernoulli variables and thus formulate the learning problem as maximization of the likelihood where latent variables are marginalized. The resulting problem is solved via variational inference, using an efficient Expectation-Maximization based method. The proposed approach improves over the state-of-the-art by automatically inferring the corruption level, while adding minimal computational overhead. We demonstrate our robust learning method and its parameter-free nature on a wide variety of machine learning tasks including online learning and deep learning where it adapts to different levels of noise and maintains high prediction accuracy.