# Learning to Sample from Censored Markov Random Fields

arXiv: https://arxiv.org/abs/2101.06178 · HTML: https://mlsanity.com/paper/2101.06178/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 15 Jan 2021.

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

- [Efficiently learning Ising models on arbitrary graphs](https://mlsanity.com/paper/1411.6156/) (2014)
- [Learning Graphical Models Using Multiplicative Weights](https://mlsanity.com/paper/1706.06274/) (2017)
- [Complexity theoretic limitations on learning DNF's](https://mlsanity.com/paper/1404.3378/) (2014)
- [Hardness of Learning Neural Networks with Natural Weights](https://mlsanity.com/paper/2006.03177/) (2020)
- [Learning Restricted Boltzmann Machines via Influence Maximization](https://mlsanity.com/paper/1805.10262/) (2018)

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

- [A Unified Approach to Learning Ising Models: Beyond Independence and Bounded Width](https://mlsanity.com/paper/2311.09197/) (2023)
- [Efficient Truncated Linear Regression with Unknown Noise Variance](https://mlsanity.com/paper/2208.12042/) (2022)
- [Learning and Testing Latent-Tree Ising Models Efficiently](https://mlsanity.com/paper/2211.13291/) (2022)
- [Perfect Sampling from Pairwise Comparisons](https://mlsanity.com/paper/2211.12868/) (2022)
- [Learning the Sherrington-Kirkpatrick Model Even at Low Temperature](https://mlsanity.com/paper/2411.11174/) (2024)
- [Learning Juntas under Markov Random Fields](https://mlsanity.com/paper/2506.00764/) (2025)

## Abstract (arXiv)

We study learning Censor Markov Random Fields (abbreviated CMRFs). These are Markov Random Fields where some of the nodes are censored (not observed). We present an algorithm for learning high-temperature CMRFs within o(n) transportation distance. Crucially our algorithm makes no assumption about the structure of the graph or the number or location of the observed nodes. We obtain stronger results for high girth high-temperature CMRFs as well as computational lower bounds indicating that our results can not be qualitatively improved.