mlsanityData to 14 Oct 2026

Learning to Sample from Censored Markov Random Fields

arXiv 2101.06178 ↗First posted 15 Jan 2021cs.LGcited by 6

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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.

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

5 of 28 references.

References · 5

  1. 2014Efficiently learning Ising models on arbitrary graphscited by 50
  2. 2017Learning Graphical Models Using Multiplicative Weightscited by 43
  3. 2014Complexity theoretic limitations on learning DNF'scited by 21
  4. 2020Hardness of Learning Neural Networks with Natural Weightscited by 15
  5. 2018Learning Restricted Boltzmann Machines via Influence Maximizationcited by 10

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 1.

6 papers.

  1. 2023A Unified Approach to Learning Ising Models: Beyond Independence and Bounded Widthcited by 14
  2. 2022Efficient Truncated Linear Regression with Unknown Noise Variancecited by 10
  3. 2022Learning and Testing Latent-Tree Ising Models Efficientlycited by 7
  4. 2022Perfect Sampling from Pairwise Comparisonscited by 6
  5. 2024Learning the Sherrington-Kirkpatrick Model Even at Low Temperaturecited by 5
  6. 2025Learning Juntas under Markov Random Fieldscited by 3