The paper proposes the ANJD flow and AVNSG operator to generate càdlàg trajectories via sequential MMD-gradient descent in Marcus-signature RKHS with generalisation bounds.
Neural stochastic differential equations: deep latent Gaussian models in the diffusion limit
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
ARL lifts states into signature-augmented manifolds and employs self-consistent proxies of future path-laws to enable deterministic expected-return evaluation while preserving contraction mappings in jump-diffusion environments.
A weak-form regression framework using spatial Gaussian kernels removes bias in recovering drift b(x) and diffusion a(x) for stochastic generators from single sparse regressions, validated on benchmarks with low coefficient and density errors.
citing papers explorer
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Generative Path-Law Jump-Diffusion: Sequential MMD-Gradient Flows and Generalisation Bounds in Marcus-Signature RKHS
The paper proposes the ANJD flow and AVNSG operator to generate càdlàg trajectories via sequential MMD-gradient descent in Marcus-signature RKHS with generalisation bounds.
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Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions
ARL lifts states into signature-augmented manifolds and employs self-consistent proxies of future path-laws to enable deterministic expected-return evaluation while preserving contraction mappings in jump-diffusion environments.
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Weak-Form Recovery of Stochastic Generators and Dynamical Invariants
A weak-form regression framework using spatial Gaussian kernels removes bias in recovering drift b(x) and diffusion a(x) for stochastic generators from single sparse regressions, validated on benchmarks with low coefficient and density errors.