mlsanityData to 14 Oct 2026

Sampling and Integration of Logconcave Functions by Algorithmic Diffusion

arXiv 2411.13462 ↗First posted 20 Nov 2024cs.DS cs.LG math.STcited by 12

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Abstract (arXiv)

We study the complexity of sampling, rounding, and integrating arbitrary logconcave functions. Our new approach provides the first complexity improvements in nearly two decades for general logconcave functions for all three problems, and matches the best-known complexities for the special case of uniform distributions on convex bodies. For the sampling problem, our output guarantees are significantly stronger than previously known, and lead to a streamlined analysis of statistical estimation based on dependent random samples.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 7 of its 37 references that are in the map. First posted on arXiv 20 Nov 2024.

Builds on

7 of 37 references.

References · 7

  1. 2019Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Sufficescited by 227
  2. 2020Structured Logconcave Sampling with a Restricted Gaussian Oraclecited by 86
  3. 2022Improved analysis for a proximal algorithm for samplingcited by 73
  4. 2022Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Spacecited by 28
  5. 2024In-and-Out: Algorithmic Diffusion for Sampling Convex Bodiescited by 19
  6. 2024Entropy contraction of the Gibbs sampler under log-concavitycited by 13
  7. 2020Reducing Isotropy and Volume to KLS: Faster Rounding and Volume Algorithmscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 9.

12 papers.