3 papers
stat.ML2026
Safely Learning Controlled Stochastic Dynamics
Luc Brogat-Motte, Alessandro Rudi, Riccardo Bonalli
We address the problem of safely learning controlled stochastic dynamics from discrete-time trajectory observations, ensuring system trajectories remain within predefined safe regi…
stat.ML2024
Learning Controlled Stochastic Differential Equations
Luc Brogat-Motte, Riccardo Bonalli, Alessandro Rudi
We study the problem of learning controlled stochastic differential equations (SDEs) \[ dX_t = b(t,X_t,u_t)\,dt + Ï(t,X_t,u_t)\,dW_t, \] whose drift and diffusion depend nonlinear…
stat.ML2024
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue +1
Leveraging the kernel trick in both the input and output spaces, surrogate kernel methods are a flexible and theoretically grounded solution to structured output prediction. If the…