mlsanityData to 14 Oct 2026

LSB: Local Self-Balancing MCMC in Discrete Spaces

arXiv 2109.03867 ↗First posted 8 Sep 2021cs.AI cs.LG stat.MLcited by 7

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Abstract (arXiv)

We present the Local Self-Balancing sampler (LSB), a local Markov Chain Monte Carlo (MCMC) method for sampling in purely discrete domains, which is able to autonomously adapt to the target distribution and to reduce the number of target evaluations required to converge. LSB is based on (i) a parametrization of locally balanced proposals, (ii) a newly proposed objective function based on mutual information and (iii) a self-balancing learning procedure, which minimises the proposed objective to update the proposal parameters. Experiments on energy-based models and Markov networks show that LSB converges using a smaller number of queries to the oracle distribution compared to recent local MCMC samplers.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 9 of its 34 references that are in the map. First posted on arXiv 8 Sep 2021.

Builds on

9 of 34 references.

References · 9

  1. 2019On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Modelscited by 105
  2. 2021Oops I Took A Gradient: Scalable Sampling for Discrete Distributionscited by 92
  3. 2017Generalizing Hamiltonian Monte Carlo with Neural Networkscited by 52
  4. 2017A-NICE-MC: Adversarial Training for MCMCcited by 38
  5. 2020Learning Discrete Energy-based Models via Auxiliary-variable Local Explorationcited by 30
  6. 2018A Framework for Adaptive MCMC Targeting Multimodal Distributionscited by 21
  7. 2015The Hamming Ball Samplercited by 16
  8. 2018Meta-Learning for Stochastic Gradient MCMCcited by 16
  9. 2021Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMCcited by 7

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Cited by year

Citing papers per year: 2022: 4; 2023: 1; 2024: 2.

7 papers.

  1. 2022A Langevin-like Sampler for Discrete Distributionscited by 44
  2. 2022Discrete Langevin Sampler via Wasserstein Gradient Flowcited by 24
  3. 2022Optimal Scaling for Locally Balanced Proposals in Discrete Spacescited by 9
  4. 2024Gradient-based Discrete Sampling with Automatic Cyclical Schedulingcited by 9
  5. 2023Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximationcited by 7
  6. 2024EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logiccited by 6
  7. 2022Improving multiple-try Metropolis with local balancingcited by 5