arXiv Machine Learning July 30, 2026 By Luc Brogat-Motte, Riccardo Bonalli, Alessandro Rudi

Learning Controlled Stochastic Differential Equations

Read the original on arXiv Machine Learning →

arXiv:2411. 01982v2 Announce Type: replace-cross Abstract: We study the problem of learning controlled stochastic differential equations (SDEs) \[ dX_t = b(t,X_t,u_t)\,dt + \sigma(t,X_t,u_t)\,dW_t, \] whose drift and diffusion depend nonlinearly on time, state, and control values.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

diffusion

arXiv Machine Learning

Sep 17

Fast Learning Rates for Physics-Informed Kernel Methods

arXiv:2609. 18901v1 Announce Type: cross Abstract: In physics-informed machine learning, a target function $u^*$ is learned from noisy value observations $y_i=u^*(x_i)+ \varepsilon_i$, together with differential information, given either by noisy observations $d_j=(Du^*)(z_j)+\xi_j$ or by a known physical constraint $Du^*=v$.

By Luc Brogat-Motte, Joachim Bona-Pellissier, Giacomo Meanti, Lorenzo Rosasco

arXiv Machine Learning

Jul 2

Goal-oriented learning of stochastic differential equations using error bounds on path-space observables

arXiv:2603. 20467v2 Announce Type: replace-cross Abstract: Stochastic differential equations (SDEs), which serve as the governing equations for dynamical systems in a broad range of applications, can become cost-prohibitive for numerical simulation at scales necessary for quantifying key properties.

By Joanna Zou, Han Cheng Lie, Youssef Marzouk

arXiv Machine Learning

Jun 25

A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients

arXiv:2606. 24999v1 Announce Type: new Abstract: High-dimensional partial differential equations (PDEs) with unknown coefficients arise widely in scientific machine learning, including continuous-time reinforcement learning, yet solving them efficiently in a data-driven way remains challenging.

By Yanwei Jia, Du Ouyang, Huy\^en Pham, Xun Yu Zhou

reinforcement-learning safety

arXiv Machine Learning

Sep 15

Convergence, design and training of continuous-time dropout as a random batch method

arXiv:2510.13134v2 Announce Type: replace Abstract: We study continuous-time dropout in controlled differential equations. We introduce a random-batch approximation of additive vector fields. On each...

By Antonio \'Alvarez-L\'opez, Mart\'in Hern\'andez

arXiv Machine Learning

Jul 10

Expressivity and Statistical Trade-offs in Diffusion Policy Learning

arXiv:2607. 07967v1 Announce Type: cross Abstract: Diffusion-based policies have recently emerged as powerful policy parameterizations for reinforcement learning, representing state-conditioned action distributions as terminal laws of diffusion processes with parameterized drifts.

By Viet Vu, Renyuan Xu, Jiacheng Zhang, Yufei Zhang

diffusion reinforcement-learning multimodal

arXiv Machine Learning

Jul 3

Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control

arXiv:2604. 08580v2 Announce Type: replace-cross Abstract: Reward fine-tuning of diffusion and flow models and sampling from tilted or Boltzmann distributions can both be formulated as stochastic optimal control (SOC) problems, where learning an optimal generative dynamics corresponds to optimizing a control under SDE constraints.

By Carles Domingo-Enrich, Jiequn Han

diffusion fine-tuning

One email a morning, machine-written

Related stories

Fast Learning Rates for Physics-Informed Kernel Methods

Goal-oriented learning of stochastic differential equations using error bounds on path-space observables

A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients

Convergence, design and training of continuous-time dropout as a random batch method

Expressivity and Statistical Trade-offs in Diffusion Policy Learning

Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control