mlsanityData to 14 Oct 2026

Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models

arXiv 2605.22795 ↗First posted 21 May 2026stat.ML cs.AI cs.LGcited by 4

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Abstract (arXiv)

We analyze finite-particle drifting models for one-step generative modeling. For a conservative velocity given by the difference of the kernel-smoothed data and model scores, a joint-entropy identity yields continuous-time bounds for the smoothed Fisher discrepancy and the squared particle velocity. The finite-particle correction involves reciprocal kernel density estimates. We give local-occupancy conditions and exact expectation bounds, and show that these expectations diverge for full-support initial densities in a shrinking-bandwidth regime. Keeping the bandwidth dependence of the quadrature constants explicit yields a conditional root residual-velocity rate of N-1/(d+4) under uniform regularity, with a corresponding rate under weaker quadrature growth conditions. We also analyze the original displacement field with the exact Laplace kernel. A companion kernel gives a weighted coercivity estimate that accommodates the unbounded empirical scale and the kernel's nondifferentiability. Localization controls the particle velocity, while relative scale alignment absorbs the mismatch into dissipation. Both analyses quantify the size of an additional drift correction under explicit regularity and stability assumptions.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 24 of its 28 references that are in the map. First posted on arXiv 21 May 2026.

Builds on

24 of 28 references.

References · 24

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2022Flow Matching for Generative Modelingcited by 5,702
  4. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  5. 2023Consistency Modelscited by 1,822
  6. 2022Building Normalizing Flows with Stochastic Interpolantscited by 995
  7. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 507
  8. 2017Stein Variational Gradient Descent as Gradient Flowcited by 152
  9. 2026Generative Modeling via Driftingcited by 116
  10. 2024Flow map matching with stochastic interpolants: A mathematical framework for consistency modelscited by 109
  11. 2015Measuring Sample Quality with Stein's Methodcited by 94
  12. 2017Learning to Draw Samples with Amortized Stein Variational Gradient Descentcited by 28
12 more
  1. 2026Sinkhorn-Drifting Generative Modelscited by 24
  2. 2022A Finite-Particle Convergence Rate for Stein Variational Gradient Descentcited by 22
  3. 2026Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspectivecited by 20
  4. 2026Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergencescited by 17
  5. 2024Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descentcited by 14
  6. 2026Drifting Fields are not Conservativecited by 13
  7. 2026On the Wasserstein Gradient Flow Interpretation of Drifting Modelscited by 11
  8. 2026A Long-Short Flow-Map Perspective for Drifting Modelscited by 10
  9. 2023The Score-Difference Flow for Implicit Generative Modelingcited by 9
  10. 2026Kernel-Gradient Drifting Modelscited by 7
  11. 2026Finite-Particle Rates for Regularized Stein Variational Gradient Descentcited by 5
  12. 2026Learning Monge maps with constrained drifting modelscited by 3

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Citing papers per year: 2026: 4.

4 papers.