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cs.LG2025
Fractional Diffusion Bridge Models
Gabriel Nobis, Maximilian Springenberg, Arina Belova +5
We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework driven by an approximation of the rich and non-Markovian fractional Brownian mot…
cs.LG2025
Efficient Training of Neural SDEs Using Stochastic Optimal Control
Rembert Daems, Manfred Opper, Guillaume Crevecoeur +1
We present a hierarchical, control theory inspired method for variational inference (VI) for neural stochastic differential equations (SDEs). While VI for neural SDEs is a promisin…
cs.LG2023★ 2 cited
Variational Inference for SDEs Driven by Fractional Noise
Rembert Daems, Manfred Opper, Guillaume Crevecoeur +1
We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM)…