Data geometry makes time identifiable from noisy interpolants at rate O(1/sqrt(d-k)), rendering the time-blindness gap asymptotically negligible relative to coupling variance.
Journal of the American Statistical Association106(496), 1602– 1614 (2011)
9 Pith papers cite this work, alongside 387 external citations. Polarity classification is still indexing.
9
Pith papers citing it
387
external citations · OpenAlex
citation-role summary
background 2
citation-polarity summary
years
2026 9verdicts
UNVERDICTED 9roles
background 2polarities
background 2representative citing papers
The Laplace-Fisher Gate Identity supplies the variance-optimal matrix blending coefficients for Tweedie and target-score estimators under an OU diffusion, enabling improved finite-reference score estimation and posterior density surrogates.
Ω is a generative assimilation method that learns residual discrepancies from ensemble data using a conditional Gaussian baseline, then reconstructs full non-Gaussian posteriors via Gaussian mixtures and annealed Langevin sampling.
Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.
Stable size extrapolation in local score models requires the receptive field to cover the quasi-locality range of the Gaussian-smoothed score, formalized via a size-uniform comparison theorem and validated on the new FDLF benchmark.
Latent diffusability is quantified by decomposing the MMSE rate along diffusion trajectories into Fisher Information and Fisher Information Rate, with three geometric penalties (dimensional compression, tangential distortion, curvature injection) identified as sources of failure.
A multiscale optimization method using explicit protein backbone geometry reconstructs atomic models from cryo-EM data, showing improved RMSD and TM scores on three simulated datasets.
Compares post-selection estimators for the maximum observed Sharpe ratio using simulations and finds James-Stein shrinkage yields lowest bias and RMSE across tested parameters.
A VAE-based latent diffusion model trained on incomplete data maintains sample quality and imputation performance up to 50% missingness while pixel-space diffusion degrades.
citing papers explorer
-
What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching
Data geometry makes time identifiable from noisy interpolants at rate O(1/sqrt(d-k)), rendering the time-blindness gap asymptotically negligible relative to coupling variance.
-
Laplace-Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
The Laplace-Fisher Gate Identity supplies the variance-optimal matrix blending coefficients for Tweedie and target-score estimators under an OU diffusion, enabling improved finite-reference score estimation and posterior density surrogates.
-
$\Omega$: Operator-based Mixture Ensemble for Generative Assimilation
Ω is a generative assimilation method that learns residual discrepancies from ensemble data using a conditional Gaussian baseline, then reconstructs full non-Gaussian posteriors via Gaussian mixtures and annealed Langevin sampling.
-
Nonparametric Riemannian Empirical Bayes, and Denoising Measurements on Manifolds
Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.
-
When Do Local Score Models Extrapolate Across Size? A Diagnostic Theory and Benchmark
Stable size extrapolation in local score models requires the receptive field to cover the quasi-locality range of the Gaussian-smoothed score, formalized via a size-uniform comparison theorem and validated on the new FDLF benchmark.
-
Understanding Latent Diffusability via Fisher Geometry
Latent diffusability is quantified by decomposing the MMSE rate along diffusion trajectories into Fisher Information and Fisher Information Rate, with three geometric penalties (dimensional compression, tangential distortion, curvature injection) identified as sources of failure.
-
Multiscale reconstruction of protein conformations from cryo-EM images
A multiscale optimization method using explicit protein backbone geometry reconstructs atomic models from cryo-EM data, showing improved RMSD and TM scores on three simulated datasets.
-
Post Selection Estimation of Sharpe Ratios
Compares post-selection estimators for the maximum observed Sharpe ratio using simulations and finds James-Stein shrinkage yields lowest bias and RMSE across tested parameters.
-
Latent Diffusion for Missing Data
A VAE-based latent diffusion model trained on incomplete data maintains sample quality and imputation performance up to 50% missingness while pixel-space diffusion degrades.