A single-network implicit neural optimal transport method that solves the c-transform via proximal fixed-point iteration for stable, non-adversarial training.
arXiv preprint arXiv:2304.13534 , year =
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Introduces a path-space stochastic control formulation for diffusion posterior sampling with time reparameterization and trust-region optimization to achieve more accurate sampling and importance-weighted corrections.
A McKean-Vlasov FBSDE generative model learns stochastic path laws that match observed terminal and time-marginal distributions via soft energy constraints rather than hard interpolation.
A new min-max robust formulation for mean field control and variational mean field games is introduced, with existence, uniqueness, and a stochastic maximum principle established under convexity-concavity assumptions.
citing papers explorer
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Implicit Neural Optimal Transport via Fixed-Point Optimization
A single-network implicit neural optimal transport method that solves the c-transform via proximal fixed-point iteration for stable, non-adversarial training.
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A Stabilized Path-Space Approach to Diffusion-Based Posterior Sampling
Introduces a path-space stochastic control formulation for diffusion posterior sampling with time reparameterization and trust-region optimization to achieve more accurate sampling and importance-weighted corrections.
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Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach
A McKean-Vlasov FBSDE generative model learns stochastic path laws that match observed terminal and time-marginal distributions via soft energy constraints rather than hard interpolation.
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Robust mean field control: stochastic maximum principle and variational mean field games
A new min-max robust formulation for mean field control and variational mean field games is introduced, with existence, uniqueness, and a stochastic maximum principle established under convexity-concavity assumptions.
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