# Robust estimation of tree structured models

arXiv: https://arxiv.org/abs/2102.05472 · HTML: https://mlsanity.com/paper/2102.05472/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Learning a Tree-Structured Ising Model in Order to Make Predictions](https://mlsanity.com/paper/1604.06749/) (2016)
- [Predictive Learning on Hidden Tree-Structured Ising Models](https://mlsanity.com/paper/1812.04700/) (2018)
- [Robust estimation of tree structured Gaussian Graphical Model](https://mlsanity.com/paper/1901.08770/) (2019)
- [Robust Estimation of Tree Structured Ising Models](https://mlsanity.com/paper/2006.05601/) (2020)
- [Latent tree models](https://mlsanity.com/paper/1708.00847/) (2017)

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

- [Robustifying Algorithms of Learning Latent Trees with Vector Variables](https://mlsanity.com/paper/2106.00885/) (2021)
- [Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise](https://mlsanity.com/paper/2102.08554/) (2021)
- [Robust Model Selection of Gaussian Graphical Models](https://mlsanity.com/paper/2211.05690/) (2022)

## Abstract (arXiv)

Consider the problem of learning undirected graphical models on trees from corrupted data. Recently Katiyar et al. showed that it is possible to recover trees from noisy binary data up to a small equivalence class of possible trees. Their other paper on the Gaussian case follows a similar pattern. By framing this as a special phylogenetic recovery problem we largely generalize these two settings. Using the framework of linear latent tree models we discuss tree identifiability for binary data under a continuous corruption model. For the Ising and the Gaussian tree model we also provide a characterisation of when the Chow-Liu algorithm consistently learns the underlying tree from the noisy data.