Wasserstein Lagrangian Mechanics formalizes second-order dynamics in Wasserstein space and provides an algorithm to learn them from observed marginals without specifying the Lagrangian, outperforming gradient flows on various dynamics.
Technical University of Denmark , volume=
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
Diffusion model priors enable training-free Bayesian sampling for more accurate rain field reconstruction from path-integrated commercial microwave link measurements than Gaussian process baselines.
A direct sampler for the global scale parameter in collapsed Gibbs sampling for horseshoe-type sparse regression, enabled by strategic spectral decompositions computed once per scan.
New extended-variable relaxations are derived for CGMESP that generalize prior bounds for CMESP and binary D-optimality and are tested numerically inside branch-and-bound.
A matrix normal extension of the Heckman model with ECM algorithm for multiple selection outcomes and SUN distribution links.
SignatureTensors.jl is a new Julia package that computes signature tensors of paths, supporting both exact symbolic and numerical computations via compatibility with the OSCAR computer algebra system.
citing papers explorer
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A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
Wasserstein Lagrangian Mechanics formalizes second-order dynamics in Wasserstein space and provides an algorithm to learn them from observed marginals without specifying the Lagrangian, outperforming gradient flows on various dynamics.
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Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Diffusion model priors enable training-free Bayesian sampling for more accurate rain field reconstruction from path-integrated commercial microwave link measurements than Gaussian process baselines.
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Spectral Collapsed Gibbs Sampler for Bayesian Sparse Regression
A direct sampler for the global scale parameter in collapsed Gibbs sampling for horseshoe-type sparse regression, enabled by strategic spectral decompositions computed once per scan.
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Extended-variable relaxations for the constrained generalized maximum-entropy sampling problem
New extended-variable relaxations are derived for CGMESP that generalize prior bounds for CMESP and binary D-optimality and are tested numerically inside branch-and-bound.
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Multiple Heckman Selection Model
A matrix normal extension of the Heckman model with ECM algorithm for multiple selection outcomes and SUN distribution links.
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SignatureTensors.jl: A Package for Signature Tensors in Julia
SignatureTensors.jl is a new Julia package that computes signature tensors of paths, supporting both exact symbolic and numerical computations via compatibility with the OSCAR computer algebra system.