Explicit closed-form solution for static KL-unbalanced OT between non-degenerate Gaussians with quadratic cost and two-sided marginal relaxations, without coupling entropy; minimizer is scaled Wasserstein coupling of adjusted marginals supported on affine graph with covariance solving Riccati.
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Newton's recursive mixture estimator is a discrete gradient flow on the Fisher-Rao manifold of probability measures.
A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.
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Closed Forms for Gaussian Kullback--Leibler Unbalanced Optimal Transport without Coupling Entropy
Explicit closed-form solution for static KL-unbalanced OT between non-degenerate Gaussians with quadratic cost and two-sided marginal relaxations, without coupling entropy; minimizer is scaled Wasserstein coupling of adjusted marginals supported on affine graph with covariance solving Riccati.
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Newton's Algorithm as a Gradient Flow: A Geometric Framework for Recursive Mixture Estimation
Newton's recursive mixture estimator is a discrete gradient flow on the Fisher-Rao manifold of probability measures.
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A unified perspective on fine-tuning and sampling with diffusion and flow models
A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.