From the 1 of 11 linked papers with an AI index.
11 papers
Learning Ergodic Dynamical Systems from a Finite Trajectory
Oleksii Kachaiev, Silvia Villa, Lorenzo Rosasco
We consider the problem of learning from a single finite trajectory of an ergodic stochastic dynamical system. More precisely, we study discrete-time autonomous stochastic systems…
Langevin for Nonconvex Optimization: Exact, Inexact and Zeroth-Order
Emanuele Naldi, Marco Rando, Lorenzo Rosasco +1
We study Langevin-based methods for non-convex optimization under smoothness and dissipativity assumptions. Our focus is on obtaining non-asymptotic bounds for the expected excess…
Learning to control switching nonlinear systems with Koopman operator regression
Edoardo Caldarelli, Oleksii Kachaiev, Cesare Molinari +1
The paper proposes using Koopman operator regression in a reproducing kernel Hilbert space to identify and control nonlinear systems with finite action spaces, creating a linear sw…
Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control
Edoardo Caldarelli, Franco Coltraro, Adrià Colomé +2
Robotic cloth folding is a challenging task, particularly when considering dynamic folding tasks, which aim at folding cloth by fast motions that leverage its dynamics. When subjec…
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
Hippolyte Labarrière, Cesare Molinari, Silvia Villa +1
Black-Box Variational Inference (BBVI) typically relies on Stochastic Gradient Descent (SGD) to optimize the Evidence Lower Bound (ELBO). However, the stochastic gradients in BBVI…
On the Sample Complexity of Learning for Blind Inverse Problems
Nathan Buskulic, Luca Calatroni, Lorenzo Rosasco +1
Blind inverse problems arise in many experimental settings where both the signal of interest and the forward operator are (partially) unknown. In this context, methods developed fo…