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=
8 Pith papers cite this work. Polarity classification is still indexing.
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Balanced designs that balance covariates (especially blocking) are asymptotically variance-optimal for binary ATE under Neyman's nonparametric model, and a CMH-based variance estimator is finite-sample conservative and asymptotically tight under local alternatives.
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.
Partitioned Gaussian sketching for distributed OLS has exact excess loss B_θ that is comparable to whole-data sketching when subset-covariance divergence D is near d.
Bayesian inverse problem with diffusion model priors for CML-based rain field reconstruction outperforms baselines by preserving rainfall statistics better than Gaussian processes.
SignatureTensors.jl is a new Julia package for computing and learning path signature tensors, integrated 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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Optimal Designs with Robust Inference for Binary Treatment Effects
Balanced designs that balance covariates (especially blocking) are asymptotically variance-optimal for binary ATE under Neyman's nonparametric model, and a CMH-based variance estimator is finite-sample conservative and asymptotically tight under local alternatives.
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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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Distributed Sketching on Data Partitions for OLS Regression
Partitioned Gaussian sketching for distributed OLS has exact excess loss B_θ that is comparable to whole-data sketching when subset-covariance divergence D is near d.
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Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Bayesian inverse problem with diffusion model priors for CML-based rain field reconstruction outperforms baselines by preserving rainfall statistics better than Gaussian processes.
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SignatureTensors.jl: A Package for Signature Tensors in Julia
SignatureTensors.jl is a new Julia package for computing and learning path signature tensors, integrated with the OSCAR computer algebra system.