STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
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Unbalanced optimal transport and unbalanced density control for Gaussian measures reduce exactly to finite-dimensional optimizations over masses, means, covariances, and SDP-solvable covariance steering with closed-form mass updates.
A lifted second-moment formulation yields an SDP for covariance steering of MJLS with multiplicative noise and convex surrogates for chance constraints.
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Stochastic Transition-Map Distillation for Fast Probabilistic Inference
STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
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Globally Solving Unbalanced Optimal Transport and Density Control for Gaussian Distributions
Unbalanced optimal transport and unbalanced density control for Gaussian measures reduce exactly to finite-dimensional optimizations over masses, means, covariances, and SDP-solvable covariance steering with closed-form mass updates.
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Covariance Steering of Discrete-Time Markov Jump Linear Systems with Multiplicative Noise
A lifted second-moment formulation yields an SDP for covariance steering of MJLS with multiplicative noise and convex surrogates for chance constraints.