Derives a conditional-marginal entropy-rate objective for bridge-aware discretization that yields U-shaped schedules and improves low-NFE sample quality on 2D, CIFAR-10, and protein tasks.
A survey of the schrödinger problem and some of its connections with optimal transport.Discrete and Continuous Dynamical Systems - A, 34(4):1533–1574
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The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
A direct plug-in kernel estimator for Schrödinger bridge time-series drifts achieves uniform non-asymptotic bounds, pointwise CLT under undersmoothing, and minimax-rate optimal adaptive selection.
Reformulates Schrödinger problem on metric graphs via entropic OT with Γ-convergence to OT, derives dynamic version converging to Wasserstein distance and geodesics, and proves existence for general data.
Proposes and analyzes a homogeneity test using squared L2 distance of empirical EOT maps to uniform-on-ball reference, with FCLT, Gaussian quadratic null limit, consistency, local power, and weighted multiplier bootstrap.
Two-parameter flows learn base-to-marginal transports via conditional flow matching then extract unique physics-time velocities by regression on synthetic trajectories, inheriting regularity and scaling to high dimensions while permitting non-gradient dynamics.
NGIF leverages gauge freedom in the continuity equation to learn non-gradient vector fields, improving distributional accuracy and non-potential transport capture over gradient-restricted baselines on low- and high-dimensional physics problems.
USB solves the Branching Schrödinger Bridge problem to enable simulation-free inference of stochastic discrete branching dynamics from single-cell omics snapshots.
A literature review synthesizing developments in quantum Wasserstein distances, their applications, and unresolved questions.
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