mlsanityData to 14 Oct 2026

Non-Vacuous Certification of Transport MCMC via Oscillation-Controlled Normalizing Flows

arXiv 2606.01078 ↗First posted 31 May 2026cs.LG stat.CO stat.MEcited by 1

Not on the map. Why?

Abstract (arXiv)

Transport MCMC trains a normalizing flow to precondition Metropolis--Hastings proposals, achieving high empirical efficiency on challenging posteriors; yet no prior work produces a numerically non-vacuous, rigorous spectral-gap bound for such samplers. We establish the first such bounds. For independence MH on the banana family we certify (γ^ast = 0.828) at (D = 2) (covering in the original space) and (γ^ast ≥ 7.6× 10-4) at (D = 5) (covering in an analytically unwarped Gaussian space with a grid-certified gradient bound under the stated numerical Lipschitz certification), both rigorous at 95% confidence. The framework rests on three pillars: (i) spectral normalization with reduced scale clips constrains the flow Lipschitz constant from (1047) to (104); (ii) a coverage-based empirical oscillation bound replaces the vacuous analytical bound with a data-dependent certificate; and (iii) oscillation-regularised training cuts the empirical oscillation by 60--90% at no cost to density fit, extending practical certificates through (D = 20) ((γ^ast ≥ 1.7× 10-4)). Tests on four further targets (Gaussian mixture, shear-building, Neal's funnel, Bayesian logistic regression) identify three precise barriers: boundary curvature, target stiffness, and tail-coverage mismatch. An affine-vs-spline comparison shows that simpler architectures yield tighter certificates at identical NLL, inverting the usual expressiveness hierarchy.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 33 references that are in the map. First posted on arXiv 31 May 2026.

Builds on

13 of 33 references.

References · 13

  1. 2015Variational Inference with Normalizing Flowscited by 2,170
  2. 2016Density estimation using Real NVPcited by 2,042
  3. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
  4. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  6. 2019Neural Spline Flowscited by 596
  7. 2018Lipschitz regularity of deep neural networks: analysis and efficient estimationcited by 415
  8. 2019Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networkscited by 293
  9. 2019Residual Flows for Invertible Generative Modelingcited by 257
  10. 2018Sorting out Lipschitz function approximationcited by 213
  11. 2019Rank-normalization, folding, and localization: An improved widehatR for assessing convergence of MCMCcited by 93
  12. 2019NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transportcited by 74
1 more
  1. 2020Understanding and Mitigating Exploding Inverses in Invertible Neural Networkscited by 71

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.