Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models
stat.ML, cs.AI, cs.LG, math.ST, stat.TH
Submitted: 2026-05-21
Updated: 2026-09-21
License: http://creativecommons.org/licenses/by/4.0/
The gist: We analyze finite-particle drifting models for one-step generative modeling.
Terminology
Abstract
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.
Sources
- Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences
- Generative Modeling via Drifting
- Learning Monge maps with constrained drifting models
- Kernel-Gradient Drifting Models
- Drifting Fields are not Conservative
- On the Wasserstein Gradient Flow Interpretation of Drifting Models
- Sinkhorn-Drifting Generative Models
- Finite-Particle Rates for Regularized Stein Variational Gradient Descent
- A Unified View of Score-Based and Drifting Models
- Identifiability and Stability of Generative Drifting in the Companion-Elliptic Kernel Family
- A Long-Short Flow-Map Perspective for Drifting Models
- Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective
- Lookahead Drifting Model
Related papers
- Behavior of prediction performance metrics with rare events
- Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
- A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
- One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
- Online Conformal Prediction for Non-Exchangeable Panel Data
- Deep Time-Series Forecasting in 10 Years: A Survey