Conditional wasserstein distances with applications in bayesian ot flow matching
In inverse problems, many conditional generative models approximate the posterior
measure by minimizing a distance between the joint measure and its learned approximation …
measure by minimizing a distance between the joint measure and its learned approximation …
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps
Conditional simulation is a fundamental task in statistical modeling: Generate samples from
the conditionals given finitely many data points from a joint distribution. One promising …
the conditionals given finitely many data points from a joint distribution. One promising …
Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
Among generative neural models, flow matching techniques stand out for their simple
applicability and good scaling properties. Here, velocity fields of curves connecting a simple …
applicability and good scaling properties. Here, velocity fields of curves connecting a simple …
Tightening Causal Bounds via Covariate-Aware Optimal Transport
Causal estimands can vary significantly depending on the relationship between outcomes in
treatment and control groups, potentially leading to wide partial identification (PI) intervals …
treatment and control groups, potentially leading to wide partial identification (PI) intervals …