mlsanityData to 7 Oct 2026

Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models

arXiv 2608.01547 ↗First posted 3 Aug 2026stat.ML cs.IT cs.LGcited by 0

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Abstract (arXiv)

Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations. We ask what distributional conclusion such a frozen measurement system warrants. For integrable antisymmetric interactions and absolutely continuous laws in a declared finite density basis, the unnormalized sampled numerator satisfies operatornamevec(VX)=Mc, where c is an antisymmetric mismatch and M is probe-dependent. This identity yields an a posteriori total-variation (TV) upper confidence bound accounting for held-out field noise, estimated-operator error, and externally validated L1 residual radii around normalized density approximants in the span; a nonpositive observability margin returns the trivial TV bound and abstains. The audit recomputes this numerator from held-out samples; a normalized drift statistic requires a separate joint numerator--denominator analysis. For Gaussian-RBF interactions, a global envelope supports distribution-free and empirical-Bernstein radii without truncation, with companion bounds for the Laplace similarity in the original drifting objective. We characterize random-probe observability by a population Gram matrix, identify rank and symmetry degeneracies, and prove large-bandwidth collapse toward mean matching. Synthetic studies exercise Gaussian and Laplace numerators, separately prespecified bounded-vector and variance-adaptive radii, Monte Carlo-calibrated operators, nonzero residual radii around normalized finite-basis approximants, outward-rounded observability bounds, and designed abstention. A joint basis-size/dimension stress path extends evaluation through m=8. The result is a conditional diagnostic for a finite density class, or for normalized finite-basis density approximants with external residual radii, not a universal guarantee from small training drift.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 37 references that are in the map. First posted on arXiv 3 Aug 2026.

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References · 16

  1. 2015An Introduction to Matrix Concentration Inequalitiescited by 391
  2. 2016A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluationcited by 236
  3. 2026Generative Modeling via Driftingcited by 98
  4. 2016Interpretable Distribution Features with Maximum Testing Powercited by 63
  5. 2015Fast Two-Sample Testing with Analytic Representations of Probability Measurescited by 53
  6. 2017A Linear-Time Kernel Goodness-of-Fit Testcited by 41
  7. 2026Sinkhorn-Drifting Generative Modelscited by 20
  8. 2026Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspectivecited by 17
  9. 2026Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergencescited by 15
  10. 2026Drifting Fields are not Conservativecited by 11
  11. 2026A Unified View of Score-Based and Drifting Modelscited by 8
  12. 2026On the Wasserstein Gradient Flow Interpretation of Drifting Modelscited by 6
4 more
  1. 2026Kernel-Gradient Drifting Modelscited by 6
  2. 2026Kernel Tests of Equivalencecited by 2
  3. 2026DriftXpress: Faster Drifting Models via Projected RKHS Fieldscited by 2
  4. 2026Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Modelscited by 2

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