# Learning Ising Models with Independent Failures

arXiv: https://arxiv.org/abs/1902.04728 · HTML: https://mlsanity.com/paper/1902.04728/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

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

- [Outlier-Robust Learning of Ising Models Under Dobrushin's Condition](https://mlsanity.com/paper/2102.02171/) (2021)
- [A Unified Approach to Learning Ising Models: Beyond Independence and Bounded Width](https://mlsanity.com/paper/2311.09197/) (2023)
- [Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models](https://mlsanity.com/paper/2106.03969/) (2021)
- [Learning Continuous Exponential Families Beyond Gaussian](https://mlsanity.com/paper/2102.09198/) (2021)
- [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)
- [Learning Energy-Based Representations of Quantum Many-Body States](https://mlsanity.com/paper/2304.04058/) (2023)
- [Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise](https://mlsanity.com/paper/2102.08554/) (2021)

## Abstract (arXiv)

We give the first efficient algorithm for learning the structure of an Ising model that tolerates independent failures; that is, each entry of the observed sample is missing with some unknown probability p. Our algorithm matches the essentially optimal runtime and sample complexity bounds of recent work for learning Ising models due to Klivans and Meka (2017). We devise a novel unbiased estimator for the gradient of the Interaction Screening Objective (ISO) due to Vuffray et al. (2016) and apply a stochastic multiplicative gradient descent algorithm to minimize this objective. Solutions to this minimization recover the neighborhood information of the underlying Ising model on a node by node basis.